**Energy Sources from Agriculture and Rural Areas**

Editors

**Vitaliy Krupin Roman Podolets**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Editors* Vitaliy Krupin Institute of Rural and Agricultural Development, Polish Academy of Sciences (IRWiR PAN) Warsaw Poland

Roman Podolets Institute of Economics and Forecasting of the National Academy of Sciences of Ukraine Kyiv Ukraine

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Energies* (ISSN 1996-1073) (available at: https://www.mdpi.com/journal/energies/special\_issues/energy\_ sources\_agricultural\_rural\_areas).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. *Journal Name* **Year**, *Volume Number*, Page Range.

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Cover image courtesy of Vitaliy Krupin

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## **Contents**



## **About the Editors**

#### **Vitaliy Krupin**

Dr Vitaliy Krupin holds an assistant professor position in the Department of Economic Modelling at the Institute of Rural and Agricultural Development, Polish Academy of Sciences (IRWiR PAN). With a primary major in international economics and trade, over the past years he has focused on the issues of rural development, agricultural and environmental economics, renewable energy sources and climate change. He is involved in several international research projects: BioMonitor4CAP and SoilValues in the Horizon Europe program; SURE-Farm, LIFT and TRADE4SD in the Horizon 2020 program; LIFE Climate CAKE PL and LIFE VIIEW 2050 in the EU's LIFE program; and has formerly contributed to projects financed by USAID, the Ministry of Agriculture and Rural Development of Poland, and the National Academy of Sciences of Ukraine. He is an expert evaluator at the Polish Agency for Enterprise Development (PARP) and the National Research Foundation of Ukraine (NRFU). He is the author of over 150 scientific publications, among which are 12 monographs (1 personal and 11 in co-authorship), and over 50 articles in peer-reviewed scientific journals. He has been awarded for his work on rural and agricultural development by the Parliament of Ukraine (2016) and by the Minister of Agriculture and Rural Development of Poland (2022).

#### **Roman Podolets**

Dr Roman Podolets is a Ukraine-based researcher, specializing in the country for over twenty years. He heads the Energy Department at the Institute for Economics and Forecasting of the National Academy of Sciences of Ukraine, where he conducts studies and offers government consultancy on various energy and environmental issues. His most recent studies focus on the economic and energy repercussions of Russian aggression and the rebuilding of Ukraine with a resilient, carbon-neutral energy system. Dr Podolets either leads or contributes expertise to a multitude of research and consultancy projects. He also served as the Head of the Department of Corporate Management and Modelling at the Institute of Oil and Gas Industry "Naukanaftogaz" for two years, and completed an internship at DIW-Berlin, focusing on energy modelling. Author of numerous scientific and analytic publications, including in peer-review journals with high impact factors.

## **Preface to "Energy Sources from Agriculture and Rural Areas"**

This Special Issue is devoted to energy generation within rural areas, including the agricultural sector. Such technologies and application practices vary depending on the type of agricultural activity, local natural conditions, and external factors, thus globally creating a multitude of possible approaches and applications of technologies under particular conditions. We strive to go beyond strict technological perception and enrich it based on a multidisciplinary approach, making it possible to pursue an understanding not only of energy generation technologies, but also the conditions of their implementation and possible measures to increase their efficiency (technological, economic, environmental, and others).

Energy produced within or in addition to agricultural activities supplies the direct needs of farms, as well as other entities, through the energy market. Even though it is an integral part of so-called "renewable sources", agriculture's distinct features create the preconditions and necessity for research on technologies aimed at the generation of energy within agriculture itself. This is needed for both directly addressing such technologies, as well as for the substantiation of measures available to ensure the development of complex approaches towards ensuring sustainability in agriculture. It is also crucial considering the policies aimed particularly at the development of agriculture and rural areas (e.g., the EU's Common Agricultural Policy).

A multitude of economic, social, environmental, and institutional factors constantly modify the conditions for agricultural production, inflicting positive or negative effects upon its structure, output, and efficiency. Climate change issues are forcing agricultural entities to mitigate their negative effect upon the environment, while also creating the necessity to adapt and maintain proper efficiency and output levels. All of these influence the sustainability of rural areas and agriculture, both in terms of its primary production focus, as well as its input into the generation and use of energy.

> **Vitaliy Krupin and Roman Podolets** *Editors*

### *Article*

## **Crop Residue Removal: Assessment of Future Bioenergy Generation Potential and Agro-Environmental Limitations Based on a Case Study of Ukraine**

### **Sergii Kyryzyuk 1, Vitaliy Krupin 2,\*, Olena Borodina <sup>1</sup> and Adam W ˛as <sup>3</sup>**


Received: 19 September 2020; Accepted: 12 October 2020; Published: 14 October 2020

**Abstract:** This study assesses the bioenergy generation potential of crop residues in Ukraine for the year 2030. Projections of agricultural development are made based on the Global Biosphere Management Model (GLOBIOM) and verified against available Agricultural Member State Modeling (AGMEMOD) results in regard to the six main crops cultivated in Ukraine (wheat, barley, corn, sunflower, rape and soya). Two agricultural development scenarios are assessed (traditional and innovative), facilitating the projection of future crop production volumes and yields for the selected crops. To improve precision in defining agro-environmental limitations (the share of crop residues necessary to be kept on the fields to maintain soil fertility for the continuous cultivation of crops), yield-dependent residue-to-product ratios (RPRs) were applied and the levels of available soil nutrients for regions of Ukraine (in regard to nitrogen, phosphorus, potassium and humus) were estimated. The results reveal the economically feasible future bioenergy generation potential of crop residues in Ukraine, equaling 3.6 Mtoe in the traditional agricultural development scenario and 10.7 Mtoe in the innovative development scenario. The projections show that, within the latter scenario, wheat, corn and barley combined are expected to provide up to 81.3% of the bioenergy generation potential of crop residues.

**Keywords:** crop residue; bioenergy; generation potential; residue-to-product ratio; soil nutrient balance; cereals; industrial crops; GLOBIOM model; Ukraine

#### **1. Introduction**

According to the national strategic documents from 2017 [1], renewable energy is expected to play a growing role in Ukraine, reaching 15.5 Mtoe or 17% of total energy supply by 2030, while the energy generated from biomass, biofuels and waste is projected to reach 8.8% or 8 Mtoe. This is deemed crucial for the diversification of energy sources and for increasing Ukraine's independence from foreign energy suppliers, while having a favorable impact on climate change and the environment.

Currently (2018 being the latest available data), renewable sources in Ukraine generate 4.3 Mtoe or 4.6% of the total energy supply, while biomass, biofuels and waste combined generate 3.2 Mtoe or a 3.4% share [2]. To reach the goals set, an intense structural transformation of the energy sector is needed, shifting it toward renewable energy generation. Private investors (as the key actors in this transformation) must follow the indicative development path and implement the available production technologies.

As one of the leading domestic entities in the energy sector, the Bioenergy Association of Ukraine [3] states that the majority of bioenergy generation potential in Ukraine is based on the crop residues of mainly cereals and oleaginous plants (both from field and processing) and energy crops (corn, rape, energy trees, and shrubs), which—combined—provide up to 83% of the country's total biomass potential (Figure 1).

**Figure 1.** Economic potential of energy generation from biomass in Ukraine by inputs (based on 2017 data). Source: own aggregation based on Bioenergy Association of Ukraine estimations [4].

The current high energy generation potential of biomass in Ukraine has also been confirmed by other studies. In particular, [5] provides an assessment of the so-called technical sustainable potential of crop residues based on five key crops (wheat, barley, corn, sunflower and rape) equaling ca. 6.1 Mtoe (estimations based on 2013 data). In the study, the technical sustainable potential (i.e., one that ensures the required return of nutrients to the soil) is derived from the theoretical potential, and takes into account limitations for yield increases in residues due to technical constraints. In addition, sustainability constraints are considered, although the study stresses that sustainability is limited to environmental factors and that socioeconomic factors are not covered. Another study [6] by the Food and Agriculture Organization (FAO) provides an overview of different assessments of the economic potential of primary agricultural residues based on data from the first half of the 2010s, with the annual results varying within the 9.9–12.8 Mtoe range.

Energy generation based on biomass is becoming increasingly important as a low-carbon, widely available, renewable component of national energy matrices [7], with land-intensive bioenergy already constituting a significant part of the global energy mix [8]. In the past two decades, studies have made efforts to substantiate bioenergy development directions and functions [9–13] focusing on the diverse uses of biomass, and have thereby also tried to define the existing and future energy potential of biomass globally [14–18] or for particular countries [19–21]. As more and more of the peculiarities and limitations of biomass utilization become visible, increasing numbers of studies [8,22–24] are also seeking to go beyond simple assessments of bioenergy potential, additionally defining if and how its utilization will be feasible and relevant in the long run.

The generation potential of agricultural crop residues is among the most frequently discussed topics [5,25–27]. The utilization of this potential is especially beneficial to countries focusing on crop production and boasting vast agricultural areas. Ukraine is one example, boasting 41.5 million ha [28] of agricultural land and intensifying its crop specialization in its constantly growing agricultural production. This explains the considerable interest in researching crop residues' potential in Ukraine [4,6,29–36]. Nevertheless, the variety of approaches implemented in the cited studies exemplify the complexity of the issue and the existence of different methods to estimate both crop residues' availability and the bioenergy generation potential of the withdrawn biomass. Most agree that there is a continuous need to improve the methods and approaches utilized, taking into account more factors and assessing existing uncertainties.

Among the research uncertainties are future agricultural development trends; thus, many studies prefer to assess the present potential of crop residues based on the available statistical data. Diversified crop yields and their expected values in the future represent other uncertainties, as climate change, extreme weather events and the limited availability of organic and mineral fertilization have substantial influence, yet are difficult to predict. Regional disparities exist in terms of soil fertility and nutrient deficits. Overall, approaches to estimating crop residues' volumes differ depending on the research's purpose. As [37] states, agricultural crop residue estimations are often limited to multiplying crop yields by a harvest index (the ratio of non-grain plant material to grain material), yet estimates can be refined by accounting for factors that might limit their quantities.

Crucially, the removal of crop residues from fields must be managed in a balanced manner in order to satisfy the sustainability of agricultural production and development. Residues play a number of critical roles within an agronomic system, and have direct and indirect impacts on physical, chemical, and biological processes in the soil. Excessive residue removal can degrade the long-term productive capacity of soil resources [27].

Thus, bioenergy generation potential must be realized while maintaining sustainable crop production through ensuring adequate levels of soil nutrients and aiming at the optimization of mineral fertilization according to agronomic and environmental norms, as well as economic viability. This means that the key criterion for crop residue removal is the optimal withdrawal level, ensuring the balanced satisfaction of both continuous crop production and bioenergy needs. Therefore, the objective of this research is to define the future bioenergy generation potential of crop residues in Ukraine while satisfying these agro-environmental limitations.

This article is divided into five sections. Following the introduction, Section 2 explains the methods used to model Ukrainian agricultural development (serving as the basis for the estimation of selected crop production volumes), the methods used to calculate the environmental limitations for the removal of crop residues, and the methods used to estimate the energy potential of crop residues, supplemented by a description of the data sources. Section 3 presents the research results as follows: (a) a brief description of Ukrainian agriculture; (b) projections for the development of selected crops' cultivation; (c) an assessment of the availability of crop residues based on agro-environmental limitations; and (d) estimations of bioenergy generation. Subsequently, Section 4 is devoted to a discussion and comparison of the results obtained with similar studies. Finally, Section 5 presents the conclusions, implications and limitations of the work.

#### **2. Methods and Materials**

Typical agricultural crop residues in Ukraine can be divided into two main groups: (1) field residues, comprising straw, stalks, stubble, leaves, seedpods, etc.; and (2) process residues, comprising husks, seeds, roots, and bagasse. The substantiations presented in this article strictly deal with the former (also referred to as crop residues).

#### *2.1. Modeling Ukraine's Agricultural Development*

Given that the research approach has been designed to estimate the future bioenergy generation potential of crop residues, 2030 is set as the target year. Thus, it is crucial to identify the current and projected trends in the development of Ukraine's agricultural sector that will determine the potential future availability of bioenergy resources. These estimations serve as the basis for understanding the economic potential of the crop residues available for energy generation. Trends in the agricultural production of selected crops are modeled using the Global Biosphere Management Model (GLOBIOM), including elements such as crop yields, gross harvest, and regional distribution. GLOBIOM is a partial equilibrium model of the global agriculture and forestry sectors, providing data for crop and livestock production at the level of simulation units. There are two sets of variables: endogenous (e.g., process, production, land uses) and exogenous (e.g., population, GDP growth, technological changes). Prices are determined at the regional level to establish market equilibrium to reconcile demand, domestic supply, and international trade. The average yield for each crop in each major region or country is taken from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT). Potential and

average yields as well as yield variability dependent on climatic and weather conditions are provided in simulated units, generated based on suitability studies by the Environmental Policy Integrated Climate (EPIC) model. Land and other resources are allocated to different production and processing activities to maximize a social welfare function, which consists of the sum of the producer and consumer surplus, based on cost structure and input use in terms of simulated units. The model simulates four different crop management systems—irrigation, high-input/rainfed, low-input/rainfed, and subsistence farming—with different levels and structures of production cost and profitability. It is based on the following key determinants: on the one side, demand, and, on the other, the profitability of the various land-based production activities. Thus, the model allows the land use change between crops and management systems to maximize the social welfare level [38].

While the estimations for this research are conducted solely with the use of the GLOBIOM, the results are also compared to the Agricultural Member State Modeling (AGMEMOD) results available in the literature [39].

Two modeling scenarios have been developed for the purpose of this research with the use of the GLOBIOM model: business-as-usual (BAU) and innovative (INNO). The BAU scenario is based on the assumption that the current development trends in Ukrainian agriculture (covering 2000–2015) will be preserved. The INNO scenario assumes the increasing implementation of agricultural production technologies through intensifying investment (based on the possible increase in the availability of financial resources), as well as scientific development, the modernization of existing agricultural machinery, the application of more advanced agricultural technologies, the use of high-quality seeds, and improvements in infrastructure. Technological changes (exogenous variables in the model) depend on GDP growth, via the elasticity function. These changes determine the level of crops' yields, the production costs, and the profitability of the different land-based production activities. In general, the INNO scenario provides a better realization of crops' potential yields (at least 80% of potential yield for a high-input system) and the widespread implementation of the high-input management system.

#### *2.2. Methods for Evaluating the Environmental Limitations to Removal of Crop Residues*

In order to conduct agricultural activities ensuring sustainable development, it is important to satisfy the agrochemical Law of Return, which requires that the soil be compensated for the nutrients removed by crops. In addition, soil nutrient losses due to general agricultural activities need to be taken into account. Typically, the control is conducted by determining the balance of (a) humus, and (b) nutrients. The main sources of humus are the humification of crop residues and organic fertilizers, while its losses occur due to its mineralization and erosion. Nutrient uptake is usually measured on the basis of experimental reference values that take into account their content in organic and mineral fertilizers and crop residues, as well as their replenishment due to biological nitrogen fixation and precipitation. Soil nutrient losses are estimated through their removal by crops during harvest and due to soil erosion (via the processes of leaching and weathering).

In the following, the estimates for humus and nutrient balances within the cultivation of selected crops in Ukraine are calculated based on the methodology developed by the National Academy of Agrarian Sciences of Ukraine (NAASU) [40], which takes into account the aforementioned characteristics. It includes evaluations of the balances of the essential nutrients (N, P2O5, K2O) and humus. The nutrient balance is calculated as the difference between nutrient supplies and losses. Nutrients are supplied through the application of mineral and organic fertilizers, with precipitation, seeds, and symbiotic and non-symbiotic nitrogen fixation. Nutrient losses occur due to crop harvest, weeds, erosion, denitrification, and irrigation (although only for irrigated land). Similarly, the balance of humus is calculated by taking into account all sources of its accumulation (humification of crop residues, humification of organic fertilizers) and losses (humus mineralization, losses due to erosion).

#### *2.3. Energy Potential of Crop Residues*

Although numerous investigations have sought to estimate the energy potential of crop residues (many of which are referred to in this study), most [5,6,34,36,41] use similar methods based on the assessment of theoretical potential, while the next estimation steps are referred to differently by the authors (e.g., technical potential, technical sustainable potential, economic potential). Nevertheless, the key features they share are derived from the method [30] provided by the National Academy of Sciences of Ukraine (NASU). The [33] provides further development of this method, outlining three types (levels) of potential: theoretical, technical, and economic.

Theoretical potential: the overall maximum amount of terrestrial biomass that can be considered theoretically available for bioenergy generation within fundamental biophysical limits. The theoretical potential for field residues is calculated based on the maximum crop yields within the particular climate limits.

Technical potential: the fraction of the theoretical potential achievable for energy generation under the specific techno-structural conditions with the current technological possibilities (such as harvesting and processing techniques) available. It can be limited by various factors, in particular spatial allocation, competition between land users, and ecological limits. The technical potential is calculated by multiplying the theoretical potential and the coefficient of technical availability.

Economic potential: the share of the technical potential suitable for energy generation under current market and economic conditions.

The abovementioned elements are calculated as:

(a) theoretical potential:

$$P\_t = \mathbb{C}\_r \bullet \mathbb{K}\_r \bullet \mathbb{K}\_{cr}$$

(b) technical potential:

$$P\_{tr} = \mathbb{C}\_r \bullet \mathbb{K}\_r \bullet \mathbb{K}\_{cr} \bullet \mathbb{K}\_t$$

(c) economic potential:

$$P\_{\varepsilon} = \mathbb{C}\_{r} \ast K\_{r} \ast K\_{\varepsilon c} \ast K\_{t} \ast K\_{c}$$

where: *Pt*—theoretical potential; *Pte*—technical potential; *Pe*—economic potential; *Cr*—total production of crop *r*; *Kr*—residue-to-product ratio (RPR) of *r* crop; *Kt*—coefficient of technical availability of crop residue; *Ke*—coefficient for crop residues' utilization for energy purposes; *Kce*—oil equivalent for crop residues (toe); *r*—crops.

Thus, crop residues' economic potential depends on a number of the abovementioned coefficients used for the assessment. What is important to note is that there is still uncertainty within scientific publications as to their proper levels. The first uncertainty exists in the case of the residue-to-product ratio (RPR). The recommendations used nationwide, developed by the NASU, offer fixed RPRs [30]. However, both theoretical and applied studies indicate an inverse correlation between the yields of primary and secondary crops in conditions of the increasing productivity of most primary crops [5,32,34,41]; thus, yield-dependent RPR values seem to be more suitable. In addition, the influence of crop varieties and hybrids needs to be taken into account (in particular, cover crops), the cultivation of which is becoming especially important due to climate change's impacts (lower water availability, extreme weather conditions [39]). Therefore, RPRs for both approaches are presented in Table 1 for selected crops. In the second approach, the unknown *x* in the equation for the crop residue evaluation is the yield, while the variable y is the crop residue yield depending on the main crop yield, both in decitons.


**Table 1.** RPRs (fixed and yield-dependent) for selected crops.

Source: based on [30,32] (pp. 20–21).

In general, the RPRs estimated within the second approach based on empirical Ukrainian data for the period 2000–2015 are higher than the RPRs estimated based on the European Union's data [32] (pp. 12–13). These differences can be explained by the higher crop yields and different technologies, crop varieties and hybrids used in the EU. They could be used to understand possible future development trends in Ukraine in case of the implementation of similar production approaches.

Additional uncertainties in regard to RPRs persist due to existing or periodically arising challenges, such as seasonal and regional weather uncertainties, the availability of nitrogen, and the application of herbicides [5] (pp. 73–79).

Some uncertainties also occur when evaluating the coefficient of the technical availability of crop residues (*Kt*). Although it is generally characterized by a lower level of uncertainty, some variability is still present due to differences in the technologies used for harvesting (i.e., requirements for the height of the cut stem, and availability of equipment on a farm allowing the residues to be harvested). At the same time, the value of the technical potential of crop residues could be reduced by the negative effects of weather events (hail, squalls, droughts). In the aforementioned NASU recommendations [30], the value of the coefficient of technical availability is defined at the level of 0.8. Notably, the use of slightly lower values has been substantiated in some studies by Ukrainian scientists. In particular, for the Khersonska region, the coefficient of technical availability of the main grain crops based on data from 2012–2013 was estimated at 0.5, and for corn and major industrial crops at 0.7 [34] (p. 113). Another study dealing with the Sumska region defined a coefficient of technical availability of 0.5–0.7 for grains [42] (p. 113).

Concerning the coefficient for crop residues' utilization for energy purposes *Ke*, uncertainties depend on market and technical factors, including market competition and the economic efficiency of different uses of crop residues (for animal feed, as a fertilizer), the availability of infrastructure for transportation, and the availability and capacity of biomass processing for other purposes. Traditionally, the use of cereals' straw is directed at the needs of livestock (as roughage and bedding). However, the role of straw in animal nutrition (as a source of fiber) in the transition to intensive technology is minimized, retaining its functional purpose only as bedding for animals. With market changes in Ukraine, the profile of agricultural producers has changed significantly due to intensifying production specialization and a general reduction in livestock numbers. There are fewer and fewer agricultural enterprises with diversified crop and animal production. Currently, there are regional zones of different agricultural specializations in Ukraine, most notably a few livestock production zones around key urban

agglomerations [43] (pp. 131–133). Thus, several conclusions can be drawn that are important from the point of view of competition over access to agricultural residues for livestock production purposes:


Decreasing competition from the livestock sector may be partially offset by increasing demand for the use of crop residues for agro-environmental purposes within land use, aimed at restoring soil fertility. Limiting the possibility of applying organic fertilizers (natural manure) increases the role of crop residues for the restoration of the organic component of the soil: 1 ton of straw equals 2.5–2.8 tons of bedding manure in equivalent humus [35] (p. 6). However, the use of straw as a fertilizer leads to additional costs, which is mainly due to increased fuel consumption to mechanically prepare the straw for further use, and the higher application rates of nitrogen fertilizers to accelerate the mineralization of straw (10–12 kg of N per 1 ton of straw) [35].

In the last decade, demand for crop residues has been increasing due to climate change's impacts (higher temperatures and water evaporation, lower rain frequency) through the implementation of adaptation measures in crop production. Under new climate conditions, the efficiency of traditional agricultural practices (primarily based on deep tillage) has been decreasing, especially in the southern and eastern regions of Ukraine. Accordingly, the issue of the selection and implementation of alternative technologies and practices aimed at minimizing the negative impacts of weather factors has gained in importance. Such technologies include strip-, mini, or no-till practices. The use of these technologies limits the possibility of using crop residues for purposes other than as a cover material [44].

However, it should be noted that currently increasing crop yields provide higher residue outputs, creating some difficulties for those producers who apply no-till technologies. Therefore, in these cases, a certain proportion of the crop residues still needs to be removed despite the conditions of highly productive agriculture. These practices have already spread beyond the country's arid and wet regions, being actively implemented on the farms of western, central, and northern Ukraine [45]. The efficiency of no-till technologies has been confirmed by the experiences of South American countries (in particular Argentina, with 80% of its main cropland being cultivated with the use of no-till technology [46]), yet they also have certain disadvantages that reduce their attractiveness for widespread implementation. The key disadvantages are [44]:


Given the established practice of agricultural technologies in Ukraine, as well as the objective shortcomings of minimal tillage systems, a significant shift in the structure of the technologies applied by agricultural producers should not be expected in the near future. Therefore, a surplus of crop residues is expected to be available in Ukraine overall; however, due to uneven spatial distribution (regional, district, local), more comprehensive and regionally targeted approaches are required to assess the economic potential of biomass for energy generation purposes.

As for the coefficient for crop residues' utilization for energy purposes, according to the NASU recommendations, a fixed value of 0.25 is applied to all crops [30]. This value is based on the demand of livestock production and levels to maintain balanced organic matter in the soil. Under visible conditions of increasing crop productivity, it is possible to expect the achievement of a higher level of *Ke* coefficient (0.3 for cereals, 0.4 for industrial crops and corn) without harming sustainable land use, but with the application of the recommended rates of mineral and organic fertilizers [29] (p. 17). The use of the average value of the *Ke* coefficient is understandable in strategic documents at the national level, but in in-depth calculations, its values should be detailed, taking into account regional factors: spatial features of production allocation and local demand from livestock production; technologies used for crop production; types of crops; and available options to maintain the soil's fertility by biomass application (e.g., the digestate from biogas generation, the ash and sludge from biomass combustion process [47]).

#### *2.4. Data Sources*

Data on key agricultural trends (yields, area, production, nutrient supply with mineral and organic fertilizer, etc.) are provided by the State Statistics Service of Ukraine. Data on the production costs of different agricultural products are calculated based on the annual database "Main economic indicators of farm activity" are available for 2000–2015 (based on a survey with approximately 8000 farms carried out annually), which form the basis for the Ukrainian input data used by the GLOBIOM used within this study. National and regional data on soil types and their nutrient and humus contents are provided by the State Statistics Service of Ukraine. The spatial allocation of soil types is gathered from the State Service of Ukraine for Geodesy, Cartography and Cadastre [48].

#### **3. Research Results**

#### *3.1. Background: Ukrainian Agriculture and Its Transformation*

Since 1991,major transformationsinUkrainian agriculture have takenplace. Overall,livestockproduction has gradually and substantially declined (the cattle population decreasing eightfold and pig production by 3.4 times). Consequently, there are fewer opportunities available for the application of organic fertilizers in the form of manure, as was widely used in Ukraine's pre-independence era (6208 kg/ha of agricultural land in 1990 vs. 281 kg/ha in 2018 [49]). In parallel, through the 1990s there was an initially sharp reduction in the application of mineral fertilizers in crop production due to agricultural transformation processes and overall economic instability. Thus, the average application of NPK mineral fertilizers (consisting of nitrogen, phosphorus and potassium components) dropped from 105 kg/ha of agricultural land in 1990 to its lowest record of 6.7 kg/ha in 2000, and only partially regaining in intensity in the following years, reaching 56.5 kg/ha in 2018 [49]. However, crop production after the beginning of the 2000s due to the acceleration of investments (including the inflow of foreign capital) began its steady increase. Even though (since 2010) the rate of mineral fertilization has been growing, it still does not usually satisfy agronomic requirements to maintain the proper level of soil nutrients [40] (p. 97). Farming approaches and abilities vary greatly depending on a farm's type, size, and market focus. Currently, a wide range of medium and large agricultural enterprises follow an export-oriented production strategy based on cultivating a limited number of crops: wheat, barley, corn, rape, sunflower, and soya. Due to their limited access to capital, some (mostly small and medium-sized) farms do not have the capacity to maintain the necessary equipment for cultivating a diversified number of crops. Therefore, they usually grow no more than two or three crops. Such limited crop rotation leads to soil depletion. Indeed, according to previous Ukrainian soil quality studies [50], humus content decreased by 15% between 1991 and 2005.

Both 2004 and 2005 saw new intense growth in agricultural activity after 13 years of economic instability and production decline. This growth was accompanied by corporatization and the introduction of state support for agricultural producers, seeing the area used for arable land increase by 95,000 ha (0.3% of the total, albeit excluding the area of the currently occupied Autonomous Republic of Crimea) to a total of 29.9 million ha in 2017. This is still 7% less compared to 1990, yet provides evidence of growing pressure upon agricultural land resources, including those left unattended during the 1990s and the beginning of the 2000s. The area represented by pastures diminished by over 100,000 ha from 2004 to 2017. Key changes in the sown area structure since 2004 include the increase in areas (and their share in total) being used for industrial crops, structural changes in cereals (replacement of rye and barley by wheat and corn in northern regions). These key changes are summarized in Table 2.


**Table 2.** Changes in agricultural land use in Ukraine from 2004 to 2017.

Note: excluding data from the Autonomous Republic of Crimea. Source: own compilation based on [51].

Crop production currently prevails in the total agricultural output (72% in 2017), having grown by 93% from 2000 to 2017, while the growth of livestock production equals just 20%. Cereals, leguminous and industrial crops make up over two thirds of the gross crop production. Aside from having relatively high shares in Ukrainian exports, these subsectors are also providing most inputs for the production of fodder necessary for the domestic livestock sector. The aforementioned crop groups cover approximately 87% of total sown areas or 68% of total agricultural land in the country, with the six main crops (wheat, barley, corn, sunflower, rape, and soya) covering 80% and 63%, respectively [51] (Figure 2).

**Figure 2.** Sown areas and production volumes of main crops in Ukraine (based on 2017 data). Source: own compilation based on [51].

#### *3.2. Projection for the Development of Selected Crops' Cultivation by 2030*

Ukraine is among the world's leading countries with the largest unrealized agricultural potential [52–54]. According to the Global Yield Gap Atlas (GYGA), the current level of crop yields in Ukraine equal 30–45% of the estimated potential [54]. Therefore, current relatively lower yield levels could mean bigger opportunities compared to other countries concerning the possible increase in productivity in the future, even in the case the development is realized within the BAU development scenario. In addition, in the next decade the positive impact of climate change will still mostly prevail in the case of yields of major crops (excluding in south-eastern regions of the country), increasing average crop productivity relative to the baseline period.

The increasing yield forecasts derived within the GLOBIOM simulation are also confirmed by other modeling results. This is the case of the report [39] published in 2017, revealing the modeling results for Ukrainian agricultural development up to 2030, based on the AGMEMOD model. Thus, Figure 3 represents the results for the selected main crops (which combined cover approximately 90% of the total sown area in Ukraine), comprising both the data for two developed scenarios within this research (BAU and INNO) and the aforementioned AGMEMOD traditional development scenario results.

**Figure 3.** Projection of changes in yields for selected crops in Ukraine by 2030 (compared to average yields in 2008–2014). Source: Agricultural Member State Modeling (AGMEMOD) modeling results [39] and own calculations (business-as-usual (BAU), innovative (INNO)) based on the Global Biosphere Management Model (GLOBIOM).

The modeling results show the following yield growths by 2030: 10–20% for oil crops, and 18–50% for cereals in the BAU scenario. The AGMEMOD modeling confirms these projections, although, for the cereals, the expected growth is slightly lower. However, the forecasts for the AGMEMOD model tend to underestimate the available potential of cereals (Figure 4) when compared to the latest available statistical data. This provides additional reassurance concerning the credibility of the forecasts, including the innovative scenario results (INNO). The probability of the innovative scenario's realization is confirmed by the visible acceleration of yield levels from 2015 to 2019, which was possible due to the actual introduction of new crop varieties and plant protection systems minimizing the negative effects (temperature variations, precipitation reduction) while maximizing the positive impacts (higher temperatures) of climate change, thereby facilitating the increased adaptation abilities of domestic agricultural producers [55,56].

The projections of crops' production volumes obtained via the GLOBIOM approach are compared with the AGMEMOD results (Figure 5). The general trend of increasing production volumes according to the results of the GLOBIOM is consistent with the conclusions obtained within the AGMEMOD modeling, with the exceptions of barley and, to some extent, sunflower. This result can be a guide for domestic producers to make efforts to improve the economic efficiency of barley production, as its permanent growth in global demand and available production potential provides barley with a competitive advantage compared to the key cereal crop in Ukraine: winter wheat.

**Figure 4.** Wheat and corn crop yields: comparison of available statistical data and projections obtained with the AGMEMOD model. Source: own compilation based on [51], verified against [39].

**Figure 5.** Projection of production (output) change for selected crops by 2030 (compared to 2008–2014 average volumes). Source: own calculations.

#### *3.3. Assessment of Crop Residues' Availability Based on Agro-Environmental Limitations*

Based on the crop production projections, the theoretical potential of crop residues for the investigated crops (Table 3) is assessed. For this purpose, both of the abovementioned approaches to the assessment of RPRs (*Kr*) are used: *Kr*1, provided in Table 1, and *Kr*2, estimated with the use of the EU's RPR data [41].

**Table 3.** Assessment of the theoretical potential of crop residues for the selected crops in 2030, ktoe.


Source: own calculations.

The assumptions of improving Ukrainian farmers' access to technologies and financing support the expectations for the growing technical availability of crop residues. However, there is a high risk of adverse weather events that may reduce the technical availability of crop residues in certain regions and particular years covered by the projection. Given these uncertainties, the coefficient of the technical availability of crop residues at the level of 0.8 (provided by the NASU [30]) is used within the estimations.

Currently, the main share of the technical potential of crop residues is being utilized for the purposes of livestock production and stabilization of soil fertility, leaving only the remainder to be potentially used for renewable energy generation purposes. To determine future development trends within the economic potential of crop residues for energy production, some general assumptions are used for the years to come:


According to both forecasts obtained based on the GLOBIOM for the period up to 2030, a further reduction in cattle numbers is expected, with a slight (1–7%) increase in pigs and the further growth (25–40%) of poultry (depending on the scenario). At the same time, the share of all livestock kept in farm households will continue to decline according to the ongoing trends. Given these forecasts, the use of straw for livestock production purposes is expected to equal the baseline values or even decrease due to the expected gradual implementation of agricultural innovations in corporate livestock production. Accordingly, this will determine the trends in the amounts of manure available for various purposes, mainly for application to the soil as organic fertilizers. This, in turn, will engender certain risks for sustainable land use—specifically compliance with the agrochemical Law of Return. Under such conditions, the need for crop residues to maintain agro-environmental functions will increase, limiting the possibilities of its use for bioenergy generation purposes.

The need to return a proportion of agricultural residues to the fields in Ukraine owes to the low use of organic fertilizers, a consequence of the decline and disproportionate development of the livestock sector, this being the main source of organic matter for the soil due to manure humification. A deficit-free balance of humus manure application should be at least at the level of 8 t/ha in the southeastern regions, and up to 14 t/ha in the northwest when the average level in Ukraine is below 1 t/ha [50] (p. 49). Farmers try to ensure the return of nutrients by more intense mineral fertilization and by leaving 100% of crop residues in the fields. However, mineral fertilization is often either conducted in violation of agro-ecological norms of required nutrient balance, or the amount of mineral fertilization is insufficient for an adequate process of humification of crop residues.

The conducted assessment of the production processes of the main crops in Ukraine confirms the existence of issues preventing the satisfaction of deficit-free requirements. Thus, wheat production with a 100% return of crop residues ensures a positive balance of humus (H) and nitrogen (N) in most regions of Ukraine, but does not ensure the return of phosphorus (P) and potassium (K). The worst condition with the return of nutrients is observed with respect to the cultivation of barley and corn (Table 4).

The production of industrial crops also shows unbalanced soil content and an inability to balance the removed nutrients and humus, although due to the application of sufficient amounts of nitrogen-based mineral fertilizers, a positive balance for N can be achieved in the cultivation of sunflower and nitrogen-fixing soybean. Nevertheless, there is still a shortage of humus and potassium in the cases of the aforementioned crops. The cultivation of rape shows a slightly different picture: there is an acute deficit of nutrients, but, for humus, the balance of return is sufficient. This can be explained by the high output of crop root residues of rape, which, during its humification, provide the return of sufficient organic matter to the soil (Table 5).

Thus, it can be concluded that, in most regions of Ukraine, the currently applied technologies for the production of the main crops do not satisfy agro-ecological requirements, and limit the opportunity to remove crop residues for other purposes, including bioenergy generation.


**Table 4.** Soil nutrient balance for the production of cereals in Ukraine by region (calculated for 2017).

Note: N—nitrogen, kg a.i./ha; P—phosphorus, kg a.i./ha; K—potassium, kg a.i./ha; H—humus, tons/ha. Source: own calculations based on [40].



Note: N—nitrogen, kg a.i./ha; P—phosphorus, kg a.i./ha; K—potassium, kg a.i./ha; H—humus, tons/ha. Source: own calculations based on [40].

Soils with low humus content are especially vulnerable to various climatic factors, which negatively affect soil composition, structure and quality. In Ukraine, 1.7 million ha (4.1% of the country's agricultural land) is subjected to wind erosion, 13.3 million ha (32%) to water erosion, and over 2 million ha (4.8%) to both of these types of erosion. In addition, 10.7 million ha (25.8% of agricultural land) is classified as acidic, 1.7 million ha (4.1%) as saline, and 1.9 million ha as waterlogged soils. Over 20% of the territory of Ukraine is contaminated with various toxic compounds. Some areas are contaminated with radioactive compounds. Negative geological phenomena are common in over 50% of the country [57] (pp. 7–8).

Within the developed scenarios, the rate of mineral fertilizer application will increase gradually by 2030. The BAU and INNO scenarios are based on the gradual growth of mineral fertilizer application until 2030, at least by 50% and 85% of the base level (2017) for the farms ranking in the top 10% for intensive production technologies for these scenarios, respectively. Organic fertilization is expected to increase as well, but—on average—not more than 2–5 t/ha in the BAU and 3–7 t/ha in the INNO scenario, depending on crops.

Thus, the main sources of nutrient return are expected to be mineral fertilizers (up to two thirds of the return amount of nutrients depending on the crop) and crop residues (15–35% accordingly) for all crops except soya (due to nitrogen fixation, the returns are up to 50–60% of used nitrogen). The organic matter of the soil (humus) in conditions of limited application of organic fertilizers can be maintained only by returning the crop residues to the soil. Given that maintaining a positive balance of humus is a decisive factor for farmers, the possibility of removing part of the crop residues for energy purposes is therefore further limited. To understand these limitations, Table 6 presents the humus balance for the investigated crops within the two developed scenarios.


**Table 6.** Humus balance under conditions of residues' return to the soil, t/ha.

Note: B—BAU scenario, I—INNO scenario. Source: own calculations based on [40].

The positive balance of humus indicates the possibility of removing part of the crop residues in compliance with agro-environmental requirements. In the cases of soybean and sunflower, even within the INNO scenario, assuming the almost maximum allowable approximation of the yield to the potential level (and therefore an increase in the yield of the crop and its residues), there is a deficit of organic matter recovery in the soil. This is due to the relatively lower amount of crop root residues of soybean, and—for sunflower—a relatively lower coefficient of humification of crop stalks (0.2–0.25 for cereals and legumes, and 0.14 for sunflower). Thus, the average amount of straw that can be removed for bioenergy generation purposes varies depending on the crop, the region, and assumptions according to the developed scenarios. For example, according to the BAU scenario, the amount of crop residues available for bioenergy generation purposes varies from 0 to 3 t/ha, with a national average of 0.8 t/ha. Under the INNO scenario, the average and maximum values increase to 2 and 3.8 t/ha, respectively (Figure 6).

**Figure 6.** Amount of crop residues available for bioenergy generation purposes while satisfying agro-environmental requirements. Source: own calculations.

In addition to satisfying the agro-environmental requirements of land use, a proportion of cereals' residues (primarily wheat and barley straw) is used for livestock production purposes: the estimated demand for these purposes equals 3.6 and 2.7 million tons for the BAU and INNO scenarios, respectively. The lower demand for crop residues from livestock production corresponds with the current level in EU countries specializing in livestock. In particular, the maximum amount of straw used for livestock production purposes does not exceed 3.9 million tons in Poland, 3.8 million tons in France, and 2.9 million tons in Denmark [25].

#### *3.4. Bioenergy Generation Potential (National and Regional)*

Both developed scenarios (BAU and INNO) confirm the output growth of the investigated crops, meaning a higher potential loss of humus and nutrients (in particular due to crop harvest). Thus, taking into account the requirement to satisfy a positive balance of humus and nutrients in the soil, the total bioenergy generation potential of the analyzed crop residues in 2030 has been assessed. It is expected to equal 3643 ktoe within the BAU scenario, while, in the case of the INNO scenario, it is forecast to reach 10,723 ktoe (Table 7).



Source: own calculations.

The estimated total economic potential of crop residues available for bioenergy generation purposes is unevenly distributed throughout Ukraine. Within the BAU scenario, the largest amounts of crop residues for bioenergy generation purposes would be available in the regions of Kyivska, Cherkaska, Poltavska, Vinnytska, and Ternopilska (200–350 ktoe each). Following the assumptions of the INNO scenario, the regional distribution of future potential does not vary significantly, but the increase in the amounts of biomass available for renewable energy purposes makes it more promising in most regions of Ukraine (Figure 7).

**Figure 7.** Assessment of economic potential for bioenergy generation purposes from the main crop residues within the BAU (upper map) and INNO (bottom map) scenarios. Note: no data available for the Autonomous Republic of Crimea. Source: own calculations.

There is an understanding that these results reveal a slightly lower (compared to real availability) level of economic potential due to a number of aforementioned constraints applied within the estimations, as the calculations are based on average regional values of parameters and coefficients, as well as assumptions about the utilized technologies. If the same calculations were to be conducted using local data, the estimates of economic bioenergy generation potential would be higher, as they would involve less generalized constraints and reveal specific local situations.

Additionally, the availability of local data concerning actual crop rotations would facilitate greater precision with the estimation of the accumulation of nutrients and humus in the soil, including residues from predecessor crops, in turn increasing the possibility of extracting crop residues for the next crop rotation (e.g., sunflower, which is less demanding in regard to the Law of Return). Such an absence of nutrient flows and humus changes within the crop rotation, despite being a limitation, would have minimal impact on the results obtained. Another limitation of the research concerns the absence of actual data and assumptions regarding the regional land management systems (e.g., conventional, mini- or no-till), which affect the level of nutrients and humus changes, albeit not significantly.

#### **4. Discussion**

The Ukrainian expert community has formed an opinion about the possibility of extracting a significant proportion of agricultural residues for energy generation purposes [4,31,32]. Typically, such conclusions for Ukraine are based on the application of a scientifically established 0.25 coefficient for crop residues' utilization for energy purposes [30]. In addition, along with increasing yield levels, this ratio can grow to up to 0.3–0.4 depending on the specific crop. It has been noted [29] that the value of the coefficient is based on compliance with the principles of meeting the crop residue demand of livestock production (for feed, bedding) and the return to the soil of some of the organic matter withdrawn with the harvest. In our understanding, the use of a more flexible coefficient would be more precise. Thus, in this study, the coefficient varies by region and crop type, and primarily depends on requirements to satisfy the nutrient and humus balance in the soil. However, in order to achieve greater precision, data on future trends in land erosion, soil quality, the spatial supply of manure and other agronomical aspects would be beneficial.

This study's results correspond to a large extent with other similar studies for Ukraine, yet differ in terms of several key elements pertaining to the methods used for estimation. For example, the value of assessed economic potential under the developed INNO scenario corresponds with the future trajectory of energy potential from crop residues developed by the Bioenergy Association of Ukraine. In particular, the latter's assessment of residue energy generation potential for 2050 equals 10.8 Mtoe [29]. This amount includes the residue potential of all grain and oilseed crops, with the share of the six selected crops investigated in our study slightly exceeding 95%. More modest forecasts of the potential of crop residues are included in the Energy Strategy of Ukraine [1], totaling 5.3 Mtoe in 2030.

The results obtained regarding the economic potential of crop residues are also close to those of the International Renewable Energy Agency (IRENA) [58] analysis, which confirmed the possible availability of 11.0–18.3 Mtoe of agricultural residues, including 6.0–9.6 Mtoe of field residues in 2030 depending on the scenario: reference or REmap (Renewable Energy Roadmap). Nevertheless, the implementation of the REmap scenario assumes the several measures: the development of collection systems for agricultural residues, establishing the practice of long-term biomass supply contracts between producers and consumers, and the compulsory inclusion of a biogas plant in major new projects by agro-food companies. Implementation of these measures would currently be highly limited due to the difficult economic situation in Ukraine.

Another study [36] evaluating straw and stubble availability alone has shown that, within Europe, based on 2012 data, Ukraine has the second largest potential for energy generation from agricultural residues, and the largest potential based on a projection for 2030. It is important to note that Ukraine is seeing growth in the volumes of crop residues, while a general declining trend for straw and stubble supply for energy production was also outlined in this study.

In contrast to most of the abovementioned studies, the present investigation suggests going beyond typical projections based on current fixed yields (and therefore fixed RPRs) and, instead of assuming a general national coefficient for crop residues that need to be left on the fields, to take into account regional agro-environmental limitations based on the available soil nutrients. Thus, it becomes possible to understand which regions have the most bioenergy generation potential from crop residues, and to use it for future substantiations of regional economic and energy development strategies. Such estimations could be taken further, achieving greater precision at the regional level if specific soil fertility data could be obtained and used for calculations.

Discussions have recently taken place as to the future of bioenergy, as the limitations of its development are becoming more visible based on the experiences of economically developed countries. One study [8] (p. 274) argues that "land intensive bioenergy makes the most sense as a transitional

element of the global energy mix, playing an important role over the next few decades and then fading, probably after mid-century". We can agree with this statement, although, in our opinion, differences should also be expected depending on the particular region and the level of economic development of the country being analyzed. While the aforementioned study has presented a general global perspective, locally each country would still tend toward the most feasible option to ensure energy security and utilize the available potential. High differentiation would be present depending on economic development, technological advancement, and technical efficiency.

In this light, Ukraine can still be considered in an early development stage in regard to bioenergy generation, with a total 4.6% share (or 4.3 Mtoe) of renewable sources in the total energy supply (including 3.2 Mtoe from biofuels and waste energy combined) as of 2018 [2]. The country's dependency on foreign energy supply also remains critical (considering its unstable relations with its main long-time energy supplier, the Russian Federation [59]) as, in the same year, 36.5% of Ukraine's total energy supply was imported [60]. Decarbonization, being another key goal of the global renewable energy development goes in line with the growth of biomass utilization, yet presents itself as a complex issue [61] that needs to be considered and resolved wisely. Therefore, for Ukraine, the utilization of bioenergy generation potential is among the key perspective development directions available, and only under conditions of the efficient transformation of energy structure and the appearance of more beneficial (both economically and environmentally) energy generation technologies compared to biomass-based ones will further changes be possible.

Furthermore, the high probability of the projected intensification of crop production (within the six main types outlined in the research) can be identified for Ukraine based on the growing influence of large agriholdings in the past decade and their specialization. Focusing, in most cases, on crop production, these agriholdings are gradually accumulating agricultural land, and working to increase yields and therefore output volumes. Aiming primarily at exports, they managed to increase the exports of the crops investigated in this study over three-fold between 2005 and 2017: grains by 3.4 times (from 12.7 million tons in 2005 to 42.5 million tons in 2017), and oil crops by 6.7 times (from 0.9 to 6.0 million tons) [62]. Therefore, the existing evidence supports the projected growing capacities for the production of crop residues.

Another issue that needs to be highlighted here concerns the differences between the results of the BAU scenario developed within the GLOBIOM and the results derived from the AGMEMOD model, which was used for reference (Figures 3 and 5). The differences can be explained by the contrasting approaches taken concerning the projections of crops' yields and spatial allocation. In particular, AGMEMOD is based on the econometric function of yield, depending on the logarithm trend of yield (2008 = 0) and the expected income from crops in the regions (more details on AGMEMOD in [39]. The GLOBIOM uses the simulated crop yields from the EPIC model, which depend on biophysical yield potential and technological progress (calculated on the elasticities between yield and GDP growth).

#### **5. Conclusions**

Growth in the production of the main agricultural crops in Ukraine has been intensifying since the beginning of the 2000s, and is expected to continue in the next decade. This will be accompanied by increasing amounts of available crop residues, which could potentially result in the intense development of energy generation from agricultural biomass. These assumptions demanded in-depth analysis, utilizing several assessment approaches modified by the authors to increase the precision of the analysis and to show which elements in the existing methods could be improved.

The study has projected cultivation trends for six selected crops (wheat, barley, corn, sunflower, rape, and soya), these being the key specializations of the Ukrainian agricultural sector in the past two decades. Estimations based on the GLOBIOM (verified against the openly available AGMEMOD results) for the year 2030 included two agricultural development scenarios (traditional BAU and innovative INNO), allowing us to project the future crop production volumes and yields for the selected crops. The target year results have revealed a growth in crop output volumes, with higher

rates in the INNO scenario (assuming the implementation of intensive production technologies). The data obtained within the developed scenarios have enabled us to carry out projections regarding the expected volumes of the applicable crop residues.

The previous literature regarding crop residues has shown that despite the generally accepted guidelines for assessing the energy potential of biomass, there are many uncertainties that significantly affect estimations of the feasible level of field biomass removal, referred to in this article as the economic potential of crop residues for bioenergy generation. The main uncertainties are caused by the nonlinearity of RPR, the uncertainty of the harvesting technologies used, weather, and climatic risks. Therefore, to estimate projected crop residue volumes, yield-dependent RPRs drawn from the literature have been used, allowing for improved precision of assessment as well as taking into account national crop cultivation specificities.

The key agro-environmental limitation of the utilization of crop residues as biomass for energy generation in Ukraine is the need to preserve soil productivity through the restoration of its fertility. Under the limited availability of traditional organic fertilizers, crop residues currently represent the main source for humus recovery and the return of the nutrients withdrawn from the soil during the cultivation process. The traditional approach to assessing the bioenergy potential of crop residues is based on the assumption of an up to 25% technical availability of biomass for energy generation purposes; however, taking into account the agro-environmental requirements (primarily the Law of Return), this level varies greatly depending on the region and the crop type.

Therefore, this study has gone further to improve the assessment method for the potential withdrawal of crop residues, and has aimed to calculate the projected availability of soil nutrients (nitrogen, phosphorus, potassium and humus) for all regions of Ukraine. This then served as the basis for the estimation of the sustainable removal limits of crop residues for bioenergy generation. The conclusions based on this analysis demonstrate the need to improve the applied methodological approach to biomass potential assessment that is being used as the basis for national strategic documents in the energy policy of Ukraine.

Regarding the results, the assessment of the level of economically feasible bioenergy generation potential from crop residues for the year 2030 has indicated the possibility of extracting biomass in the equivalent of 3.6–10.7 Mtoe, depending on the scenario of agricultural development (traditional vs. innovative). This does not exceed 24% (varying between 8–24%) of the theoretical potential (total crop residue amount). The projections have also shown that, within the INNO scenario, wheat, corn and barley combined are expected to provide up to 81.3% of the bioenergy generation potential from crop residues. Although these results are comparable to several other studies, the approach utilized here offers less generalized assumptions and the possibility to take into account national crop cultivation peculiarities, as well as regional soil quality conditions.

As for the practical use of the results obtained, it needs to be stated that an expansion in bioenergy generation potential from crop residues in Ukraine while complying with agro-environmental limitations requires the intense implementation of organizational and technical innovations. In particular, it is crucial to ensure the circulation of biomass between agriculture and bioenergy generation, which is still poorly developed, thereby influencing the low output of the bioenergy generation sector and its slow development, as is apparent in terms of its share in total energy output. The specificity of such exchange determines the necessity and feasibility for development of local and regional bioenergy systems. These results demonstrate the need for the implementation of policy measures for the use of local renewable sources of energy, as planned by the Energy Strategy of Ukraine until 2035 [1]. At the same time, this study warns against the excessive removal of crop residues in Ukraine, necessitating the monitoring and maintenance of soil fertility, a function that could be overseen by the State Service of Ukraine for Geodesy, Cartography and Cadastre.

**Author Contributions:** Conceptualization, S.K., O.B. and V.K.; methodology, S.K. and O.B.; formal analysis, S.K., V.K.; investigation, S.K., V.K., O.B. and A.W.; data curation, S.K.; writing—original draft preparation, S.K., V.K., O.B.; writing—review and editing, S.K., V.K., O.B., A.W.; visualization, S.K. and V.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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## *Article* **Renewable Energy Utilization in Rural Residential Housing: Economic and Environmental Facets**

#### **Aleksandra Siudek 1, Anna M. Klepacka 1,\*, Wojciech J. Florkowski <sup>2</sup> and Piotr Gradziuk <sup>3</sup>**


Received: 24 November 2020; Accepted: 15 December 2020; Published: 16 December 2020

**Abstract:** Energy and climate policies benefit from modernized construction technology and energy supply source choices. Energy-efficiency improvement and CO2 emission reduction will result from renewable energy (RE) utilization in new and retrofit single-family houses in rural Poland. Several house construction scenarios and heating energy sources comparing building costs and potential emission reduction are based on already existing structures calculated for a 100 m<sup>2</sup> dwelling corresponding to the average rural home. With the addition of thermal insulation and RE-generating equipment, construction costs increase, but the energy costs of operating the home dramatically shrink between a conventional and energy-neutral house. The latter scenario includes thermal solar panels and a heat pump as heating energy sources as well as electricity-generating PV panels. Replacing coal with environmentally-friendly RE reduces CO2 emissions by about 90% annually. Additionally, lower dependence on coal lessens other GHG emissions leading to immediate air quality improvement. New house building regulations guide homeowner construction and heating energy choice, but even larger gains could result from retrofitting existing rural houses, expanding environmental benefits and generating energy bill savings to households. However, the varying climate throughout Poland will require the purchase of energy in winter to assure residents' comfort.

**Keywords:** renewable energy; rural residential housing; emission reduction; construction regulations; Poland

#### **1. Introduction**

The decarbonization of the economy requires multipronged efforts encouraging wide adoption of energy-efficient technologies and the increased utilization of renewable energy (RE) sources. Poland's policies fall in line with European Union (EU) goals and pursue the decarbonization of the economy [1]. The reduction of fossil fuels is part of the energy and climate change policy in Poland, the country most dependent on coal for the production of energy in the European Union [2]. Lowering the use of coal is also the goal of the country's air quality improvement policy because of the substantial negative health outcomes caused by toxic emissions [3]. The dramatic re-structuring of industry in Poland in the last decades led to large reductions in using coal and lowered GHG emissions, especially CO2 which decreased from 11.164 ton per capita in 1989 to 7.876 ton per capita in 2016 [4]. However, climatic conditions necessitate heating living spaces in homes and apartments from fall until early spring, and even during cool periods at other times of the year. The needs for heating energy vary across the country [5], but are particularly important to occupants of single-family homes, which prevail in rural areas of Poland and are heavy users of fossil fuels. Rural residents lack

opportunities to access piped heating used by 40% of urban households in 2018 [6]. Improvement of life quality and utilization of locally available resources such as RE are the goals of the Strategy of Sustainable Development on Rural Areas, Agriculture and Fisheries for the period 2012–2020 see for example [7]. This strategy reflects the commitment to sustainable development stated in the Polish Constitution (1997, Chapter 1, art. 5).

Heating energy accounts for the largest portion of all household energy expenditures. In the past decade, programs supporting RE utilization by households provided generous subsidies for the purchase and installation of thermal solar panels and rural households participated in the program motivated by energy cost savings [8]. However, the utilization of solar energy installations for single-family homes remains relatively limited. In 2018, one in 52 households used solar energy in Poland [6]. More recently, homeowners could take advantage of subsidies and low-cost loans through participation in a program involving the replacement of coal-using boilers with energy-efficient heating systems including wood pellet furnaces [9]. Such programs reduce heating energy needs in single-family homes, but those needs strongly depend on the construction technology used to build the house. In this context, the adoption of new construction technology mandated by new building regulations takes the concept of sustainable development to a new level. The regulations change construction methods which result in reduction of energy needs of households.

The new regulations are one element of policies aimed at increasing energy security, GHG emission reduction, utilization of RE, and encouraging modern thermal insulation methods in the construction of single-family homes in Poland. The regulations resulted from the EPBD Directive on the energy efficiency of buildings and become compulsory in 2021. The regulations require all newly built houses to achieve zero-energy status, i.e., show zero energy consumption. The EPBD also applies to old buildings. According to the Directive, every potential homeowner receiving a building permit in 2021 has to meet the technical conditions (WT 2021). The new standards apply to all construction projects, including modernization or expansion of an existing building, although some details have not been finalized [10,11].

New regulations are a part of a multipronged approach at increasing energy efficiency that sustain efforts to modernize older rural housing to permanently improve living conditions, local air and environment quality, and assure energy cost savings to households. The requirements in new construction regulations improve the coherence of government programs and allow current or future homeowner to take advantage of several separate programs aiming at energy-efficiency and RE utilization. Additionally, the mandatory building regulations eliminate the resistance to RE, while the subsidy and low-cost loans encourage homeowners who expressed opinions that subsides for RE-utilizing equipment were important in their decision-making [8]. These approaches lessen rural resident opposition to the use of RE and stronger building regulations while implementing constitutionally-mandated sustainable development.

To provide insight about gains from energy-efficiency enhancement of single-family houses, this study compares investment costs, heating energy costs, and the amount of emitted CO2 and other GHGs under alternative construction technology scenarios and several renewable and non-renewable energy use setups to heat the living space and domestic hot water. Space and water heating are the main uses of heating energy by Polish households [12]. Heating energy needs result from climatic conditions which are quite complex because of the country's geographical location [13]. The current study assumes the perspective of a predominantly rural homeowner in a country where GDP per capita in PPS is below the EU average [14] complementing research on the European decarbonization pathways analyzing emission reduction and the use of RE at the aggregate level, for example, see [15].

The scenarios include a traditional house heated with coal, traditional house using natural gas, traditional house heated with wood pellets, a house that utilizes RE and thermal insulation, and an energy-neutral house built with the latest construction technology. An average rural house had approximately 108 m<sup>2</sup> of living space in 2018 [16] traditionally heated with coal. Starting in 2021, households and the homeowners will face construction regulations requiring all newly constructed

buildings to be passive in terms of energy use and equipped with RE micro-installations [17]. Owners of existing houses constructed with outdated technology and using heating systems emitting large amounts of air pollutants can enjoy major energy savings by retrofitting their dwellings by taking advantage of currently implemented government programs. Results from this study provide information for public education campaigns illustrating the cost differences of operating houses using different types of energy as well as the associated reduction in air pollution. Lower toxic emissions instantaneously improve air quality in the immediate neighborhood enhancing life quality and health outcomes and serving as another argument for convincing homeowners to use the best available technical solutions.

The study expands the existing literature that includes research on energy performance of multifamily buildings [18] and the use of gas-powered boilers to heat residential houses in northern Poland [19] or the use of hybrid central heating systems using RE in southern Poland [20]. Moreover, the study considers multiple construction technologies and alternative heating systems. New construction scenarios are supplemented by a discussion of retrofitting an existing rural home with a central heating system utilizing biomass, the most common form of RE in the EU [21]. The selected biomass heating system utilizes wood pellets, a relatively new form of sustainable fuel gaining popularity in Poland.

The study is justified by constant new construction and the existence of a large number of detached houses in rural areas of Poland (more than three million) and emphasizes opportunities for higher energy efficiency, a key element in transforming the energy system in the EU [22]. The focus on single-family houses is also motivated by the portion of households residing in single-family homes increasing by more than 3% between 2005 and 2016 reaching 38.2% [23]. The trend to live in a detached house in Poland, defies the trend towards apartment living in multi-family housing observed in western EU country-members. The trend reflects the generally smaller average living space in Poland than in many other EU countries, but the larger detached house space involves critical decisions regarding energy-efficiency and the choice of heating energy. The consideration of alternative scenarios of building technology and heating energy sources offers insights about newly constructed homes, but also provides information for educating the owners of existing detached rural houses about economic gains from investing in energy efficiency and RE utilization. The study supplements previous research showing the crucial role of engaging owners of single-family houses to lower heating energy needs by using some RE [24]. The case study also develops alternative scenarios estimating the amount of CO2 emission reduction resulting from new building regulations.

An earlier study found that the greater the benefits to rural communities in Spain, the greater the social acceptance of projects involving the use of RE [25], while a sizable portion of the public did not see obvious benefits from the long-term economic feasibility of RE use in Finland [26]. The absence of public consultation before imposing new construction regulations in Poland forcing the use of RE coincides with the limited information on the benefits and may be interpreted as lacking impartiality. Although the public has generally favorable views of RE in Poland, once the costs of RE use affect the household, attitudes change. The distinction between the general support and local perceptions should be considered suggested a study of German public [27].

#### **2. Rural Housing and Sustainable Growth**

Rural areas cover 93% of Poland's territory [28] and rural residents account for 39.9% of the total population and that share has increased 0.7% since 2018. Rural areas are associated with farming and farming dominates the rural economy, but housing construction has been rapidly growing and contributing to local economies. The drivers of the housing construction sector are the desire of many Poles to enjoy their own individual family house as well as the replacement or renovation of existing homes. The first phenomenon results from strict regulations limiting apartment and house size under the former centrally-planned economy and a chronic shortage of accommodation for the expanding population. The generation of "baby boomers" was forced to live in cramped apartments in urban areas, or share rural houses with parents and grandparents. The never-ending shortage of

construction materials severely constrained the ability to enlarge or build new houses even if the size of a rural property could allow such an expansion. The transition to a market-driven economy since 1989 removed restraining regulations, while eliminating scarcities of construction materials and lack of access to updated building technology. The new limitation is the availability of real estate in urban areas, which led to migration to nearby rural areas, where land was less expensive. The majority of the approximately 80,000 new homes built every year in Poland is located in rural areas.

Simultaneously, the abundance of construction material permitted rural residents to either replace their old house or retrofit and enlarge the existing structure. Single-family homes represent 86.3% of all housing in rural areas [29], while multi-family housing accounts for 76.5% of all urban housing [29]. The booming construction in rural areas creates new demands on the energy supply. The construction sector uses about 40% of the world's energy [30]. An average household uses about 65% of purchased energy for space heating and 16.6% for heating water for daily use in Poland. The share of energy used for lighting and cooking is relatively small, 9.8% and 8.5%, respectively. The typical rural household uses more energy than an urban household because of the difference in size. For example, the average size of a rural house living area was 108.1 m<sup>2</sup> versus 67 m<sup>2</sup> in urban areas in 2018 [29]. Although many rural residents enjoy large living areas, their incomes are often below those of urban residents which drives their search for the lowest possible energy costs [31].

With scattered settlements of low-density housing, rural areas pose a challenge in the supply of heating energy in Poland. Although 45.8% of multi-family housing in urban areas receive heat from centralized heat-generating plants, the share among rural households was 2.9% in 2018. For example, natural gas was used for space and domestic water heating only in 7.3% of rural households in 2018. The typical rural home is heated with solid fuels, primarily coal used, by 86.2% of households. Rural households used 9.9 million tons of coal and thousands of tons of wood to start the coal fire. Some households using coal-fired furnaces burn plastic and other burnable waste increasing air pollution [32]. A recently enacted regulation allows local government representatives to enter homes in Poland to verify what is being burned in boilers [31]. Inadequate insulation and inefficient furnaces contribute to heat loss and house construction technology is a major factor determining the heating energy requirements.

A sizable share of rural homes was constructed before 1961 when regulations allowed the thermal efficiency of k < 0.87. Rural homes built between 1961 and 1995 represent 51.8% of housing and had to meet higher requirements of k < 0.3. Since 1996, another 14.5% of houses were built in rural areas, still under the requirement of k < 0.3. New regulations placed in 2008 increased thermal efficiency requirements to k < 0.25 [33]. Since then, new regulations follow the guidelines adopted by the European Commission [34].

The new construction requirements provide strong incentives to use RE, e.g., solar thermal panels, wood pellet boilers, and heat pumps for home heating systems and PV panels for generating electricity. The recently introduced programs offer subsidies and low-interest loans for replacement of home heating systems and are specifically addressed to single-family homeowners and those building new homes [9]. However, the response to the program operating since January 2018 has been minor [6]. The results of this study provide evidence of the substantial long-term economic gains through cost reduction of operating a house and can be applied to popularize the program. An increased participation in the program directly achieves the goal of local air quality improvement and extends contributions to national and EU energy and climate policy implementation. As a result, the consideration of alternative building technologies with a focus on the type of energy used involves the economic, social, and environmental aspects of sustainability.

The social acceptance of new building regulations determines the future compliance and the use of RE in new homes. Moreover, once the homes constructed using the modern technology guided be recent regulations [35] appear in rural landscape, the owners of the existing houses are more likely to undertake thermal modernization of their residencies. The living comfort in an energy neutral house, the convenience of purchasing heat-generating energy, and largely eliminated disposal of ash have an

unquestionable appeal. With the social aspect of sustainability in the background, the focus shifts to the economic and environmental aspects.

#### **3. Methodology**

#### *3.1. House Construction Scenarios*

The case study presents three scenarios involving the use of different construction methods. All of the scenarios involve fully completed and closed structures and include the installation of all windows, external doors and the door to the garage. Additionally, the scenarios also include insulation of the roof. However, the energy-efficiency of the insulating materials varies depending on the scenario. Each of the single-family house scenarios is equipped with heating systems utilizing fossil fuels and different RE sources. The comparisons also include the use of electricity supplied from the grid.

#### *3.2. Building Model*

The average living space of a single-family rural home according to the National Census summary was almost 97 m<sup>2</sup> in 2002 and increased to 101.8 m<sup>2</sup> in 2011 [36]. The average living space has been gradually increasing over time and reached 108.1 m<sup>2</sup> in 2018. However, many newly built detached houses have a floor plan much larger than the existing homes as indicated by the national average of 143.5 m2 in 2018 [37]. The ever-changing living space alters heating needs, although regional climate variation may shorten or extend those needs in Poland. The current study assumes a single-family home with 100 m<sup>2</sup> of living space and the calculations provide a benchmark that allows for adjustments for specific homes. Another simplification is the application of a house plan that is a rectangle and includes a garage as a part of the building. The house is a duplex with a functional second story (attic) and a gabled roof. Such design is common in rural areas among newly built and existing homes. The typical house occupies a flat and open terrain; most newly constructed homes are not on farms and contrast with older rural houses of farming families that are typically surrounded by buildings on the same property. The model building is heated using radiators mounted on the walls. The space heating equipment also provides the hot domestic water. The building plan does not include a basement or a cellar.

#### *3.3. Construction Costs*

Information about construction costs, costs of RE micro-installations, costs of a coal-fired furnace, and exterior wall and roof insulation were obtained from publicly available sources (see Appendix A). The scenarios include separate estimates of the annual costs of supplying the family with hot domestic water and heating the living space, the two main energy-related expenses for households in Poland. Calculations use energy prices reported by the Central Statistical Office (GUS). The alternative construction scenarios include the same major elements: building the shell of the house, installing windows, doors, roof insulation, and the heating and energy supply fitting. Although prices of construction and insulation materials vary between regions, those variations are usually negligible. Any reductions in costs that suppliers can achieve through market segmentation generally do not exceed transportation costs. Some potential homeowners in rural areas, for example, may save on labor costs if they choose to perform some tasks such as the installation of roof insulation or doors, but the savings are relatively small. The summarized costs are for the same building plan, but differ in the amount, type and energy-efficiency of selected materials and, wherever applicable, the associated labor costs.

#### *3.4. Evaluation of CO2 Reduction Emission*

The indicators of CO2 emissions for various sources of energy used by a single-family dwelling were obtained from the National Center for Emission Accounting and Management [3]. In the case of coal, the emission indicator was 94.7 kg/GJ and for natural gas it was 56.1 kg/GJ. The indicator associated with electricity that a house will have to purchase, especially during the long heating season, was 93.87 kg/GJ. The study follows the Ministry of Infrastructure and Development methodology for establishing energy features of a house or a part of a house issued on 27 February 2015 [38].

#### *3.5. Calculation of Reduction of Other GHG and Particulate Emissions*

Currently, the majority of rural homes use coal in inefficient residential stoves to heat the living space and domestic water. Burning coal emits NO2 and SO2 [39] as well as particulate matter, a well-recognized problem in Poland [40]. The emissions negatively affect health [41,42], including that of children [43]. The reduction estimates of selected toxic emissions supplement the measures of environmental benefits associated with the new building regulations.

#### **4. Results**

#### *4.1. Construction and Thermal Insulation Costs*

The construction of the shell of the building includes the ground preparation, construction of foundations, external walls, chimneys, ceilings, roof construction and cover, and gutters (Table 1). Sustainable growth in single-family home construction begins with the initial investment. The use of conventional construction technology is less expensive than the energy-neutral technology, but the differences in material used to construct external walls reveal energy-efficiency gains.

The conventionally-built home uses a brick type characterized by the heat transfer coefficient U = 0.35 W/m2K and drops to U = 0.31 W/m2K for the other two scenarios (where the lower value implies better insulating properties) (Table 1). The least expensive scenario includes thin external wall thermal insulation, and the costs increase for other scenarios. The energy-efficient home has an insulation layer of material 12 cm thick, while the energy-neutral home's wall insulation is 18 cm thick with higher insulating value. The material costs increase by 50% in the energy-efficient house variant and nearly triple (an increase of 283.3%) in the energy-neutral scenario. Labor costs increase by 89.5% for the two scenarios as compared to the conventional construction, but the energy efficiency improves substantially as the heat transfer coefficient decreases from 0.28 W/m2K to 0.15 W/m2K.

Once the shell of the house is completed, the major factors contributing to the total construction costs are related to the energy efficiencies of the three types of homes (Table 1). Windows, doors and the garage door installation completes the house and allows for the interior work on the house (not considered here). A conventional house is equipped with PCV double-pane windows with the heat transfer coefficient k = 1.4. More energy efficient widows with k = 1.1 are installed under the next scenario, but triple pane windows with k = 0.85 are installed in the energy neutral house. The cost differences are substantial and compared to the traditional home, the energy-efficient scenario lists window costs, respectively, 206.6% and the energy neutral 412.8% higher. Labor costs are only 25% higher in the energy neutral case because of the more involved installation. The door selected for the conventional house costs about a third of the entrance door installed in the energy neutral home and almost 60% of the door in an energy efficient house.

The labor cost difference in mounting the garage door is large in relative terms, 33.3%, but small in absolute terms between the first and the other two scenarios (Table 1). The garage doors installed in the three scenarios differ in their insulating capacity (U = 1.6 W/m2K vs. U = 1.1 W/m2K vs. U = 0.9 W/m2K) and the model, a single vs. segmented door. The interior thermal insulation of the attic differs only in the cost of the material since the labor cost is the same under all scenarios.

The last item of closing the structure is the cost of insulating the roof. The heat transfer coefficient of the insulation, U = 0.036 W/m2K, is identical for the two scenarios and there is a difference in the thickness of the mineral wool layer (Table 1). The energy-neutral house uses a different insulation, polyurethane (PUR) foam, characterized by U = 0.023 W/m2K. With the labor costs identical for the three scenarios, the cost difference is in the amount and type of materials, and the cost more than triples (327.7% higher) in the case of the energy neutral house as compared to the conventional scenario.


*Energies* **2020**

supplementary

**Table 1.** Building stage and costs of materials and labor for three

construction

technologies

 applying different thermal insulation and heating and

, *13*, 6637



#### *4.2. Heating System and Electricity Supply*

The primary source of heating energy and hot water is a natural gas-operated boiler in the conventional home. The energy-efficient home is equipped with a gas-fired double function condensing boiler. Both scenarios imply that a rural resident has access to piped natural gas. Access to piped gas in rural areas is increasing but still infrequent in Poland. In some regions, especially those with local natural gas deposits, the use of gas-fired boilers is realistic. Rural residents could use LPG tanks that must be periodically refilled, but the weather pattern in winter months determines the refilling frequency and two gas explosions in November 2020 in homes heated by LPG indicate the possible problems in operating such systems. The differences in costs of these systems are not considered in the current discussion. The energy neutral home obtains heat energy using the geothermal heat pump, which is the primary reason for the nearly three-fold increase in equipment costs. Specifically, the boiler cost of 7200 PLN (1692 EUR) in the conventional house scenario increases to 28,000 PLN (6542 EUR), or 288.9% more, when choosing a geothermal heat pump. Interestingly, labor costs are basically the same regardless of the homeowner's choice of the heating system. Space heating involves radiators in the case of the conventional and energy-efficient house, and flat, low-temperature radiators in the case of the energy-neutral house.

The energy-efficient and energy neutral homes utilize RE in the form of solar radiation. The energy-efficient home uses thermal solar panels, while the energy neutral house uses PV panels with the capacity of 6 kW. The panels are intended as the supplementary source of energy to power the heat supply system. Under Poland's climatic conditions and depending on the region, an energy neutral house will likely require a purchase of electricity during the months when the demand for heat is particularly high because of the scarcity of solar radiation. In Poland the available solar radiation is most scarce during the periods of highest demand for space heating [58]. The energy neutral house will generate surplus electricity in other periods because the solar radiation is typically higher, while the heating needs are limited to the use of hot water. On balance, the home will offset electricity purchase with the supply of electricity to the grid.

Finally, the costs of house ventilation are lowest in the case of using a gravitational system in the conventional house, but 11 times higher when the energy-efficient house uses mechanical ventilation (Table 1). The mechanical ventilation system in an energy neutral house is even more pricey and includes the heat exchanger for the total cost 24 times higher than in the conventional home.

#### *4.3. Total Cost Di*ff*erences*

Table 2 summarizes the total costs of building a single-family 100 m2 house using the three construction technologies and three choices of the central heating system. The costs for various construction stages are listed in Polish zloty and euro. The cost of construction of the unfinished energy-efficient house is 44.5% higher than a house using traditional technology. The costs are 182.8% higher in the case of an energy neutral house. The cost of the heating system and the supplementary electricity source for an energy neutral single-family house is a staggering 353.2% greater than that of a rural house having access to piped natural gas, which reaches a fraction of the rural population.



<sup>a</sup> Assumes a single-family house has 100 m2. Note: Exchange rate as of November 15, 2020: 1 euro = 4.2807 Polish zloty [59].

#### *4.4. Heating Energy Needs under Alternative Construction Scenarios and Retrofit Options*

In the conventional house common in rural areas, the heating system utilizes coal and often serves a dual purpose of heating the space and domestic water. Maintaining the room temperature requires constant monitoring and adding coal. Coal not only generates a sizable volume of ash, but ash disposal involves additional fees. However, the annual cost of heating space and domestic water is lowest, slightly outperforming the use of natural gas (Table 3). The use of natural gas does not require constant monitoring and eliminates the removal and disposal cost of ash. Given the lack of access to natural gas in rural areas, a wood pellet boiler offers an alternative. Wood pellet is a rather novel energy source available for household use and the specialized boilers require less frequent monitoring than the coal boiler does, while the amount of ash is a fraction of that resulting from coal burning. Wood pellets generate substantial environmental benefits because they are a locally available RE supplied by manufacturers located mostly in rural areas. Moreover, the wood pellet ash can be readily applied as fertilizer [60] in landscape surrounding a single-family home. The convenience of wood pellet use is countered by the higher total costs of supplying the house with heat energy as compared to coal (30.5% more) or natural gas (22% more) (Table 3).

**Table 3.** Energy generation costs for domestic water and central heating systems for homes built using alternative technologies and using different energy sources scenarios in PLN and euro.


Note: The exchange rate on 15 November 2020 was 1 euro = 4.2807 PLN [59]. <sup>a</sup> Price of 1 kWh generated from natural gas is 0.25 PLN as listed by Viessman. <sup>b</sup> Price of 1 kWh from electricity is 0.65 based on [16].

The annual costs of heating energy are substantially less in the case of the energy-efficient house. Those costs are 44.8% less than the coal-using traditional house, the least expensive scenario (Table 3). In the case of an energy-neutral house the energy costs of heating space and domestic water amount to only 879 PLN (205 EUR), or 10.5% of the cost of heating the single-family traditional house with coal. The calculations in the current study disregard the possible costs of routine maintenance.

The heating energy needs vary dramatically for various types of houses (Appendix B, Tables A1 and A2). A traditionally built house that uses a coal-fired boiler is estimated to require 31,909 kWh heating energy per year. By switching to the use of natural gas as the energy source, the requirements drop by 37.6%. An energy efficient house that is equipped with a RE installation requires 83.4% less heating energy then the coal-heated house. Those needs drop by 86% in the case of an energy neutral house (Table A1). The cost of the annual needs of heating energy depend on prices suggesting that heating with coal is (15.6%) less expensive than using the environmentally friendly natural gas coal has been traditionally a secure and affordable energy in Poland [61]. The calculations do not account for the convenience associated with the use of natural gas and assume that the pipped gas is available at rural locations. The energy savings are slightly larger when the traditional house is heated with natural gas, but since that option is available only to a fraction of rural homes such comparison is less realistic.

The boiler heating water requires an electric pump to force water circulation. The traditional house with a coal-fired boiler necessitates 334 kWh annually of auxiliary electricity supply, 8% less than when using natural gas (Table 1). Under the considered construction scenarios, the corresponding costs of energy production drop from 4404 PLN (1029 EUR) in a traditional house that depends on coal to 766 PLN (179 EUR) for the energy efficient and energy neutral houses, or 86.4% less.

#### *4.5. Changes in CO2 Emission*

The sustainability principle is well served by the reduction in emissions stemming from the use of modern construction technology and heating energy source. The traditional coal heated house emits about 165% times more CO2 than a similar house heated with natural gas (Table 4). The traditional coal-using house considered in this study already includes energy-efficiency supporting upgrades such as insulated windows, doors, external wall insulation and an insulated roof. However, among more than three million single-family houses in rural areas, many still have not completed such upgrades, while using the inefficient coal-burning boilers.


**Table 4.** Annual emissions for single-family rural house construction scenarios.

<sup>a</sup> g/kg of burned wood pellet.

Switching to a wood pellet boiler nearly eliminates CO2 emissions because the burning recycles the gas already absorbed by wood from the atmosphere [62]. The only emissions associated with the use of wood pellet boiler is the electricity needed to operate it causing that heating RE energy option to emit 0.8 kg/m2/year.

A house built with energy-efficiency in mind and enabled to use RE in the form of thermal solar panels generates 87.5% less CO2 than a traditional house heated with coal (Table 4). The energy neutral house releases 3.5% of CO2 volume emitted by a traditional house heating with coal but more than a house equipped with a wood pellet boiler (Table 4).

#### *4.6. Changes in Toxic GHG and Particulate Emissions*

Rural houses in Poland that use the inefficient coal furnaces or boilers are a source of emissions including SO2, NO2, PM2.5, and PM10. The health effects of those emissions are wide in scope and well established. For example, rural children exposed to GHG and particulate matter (PM) have higher risk of developing Type 1 diabetes [63]. The energy neutral house does not emit any toxic gases to its immediate neighborhood. The amount and composition of emissions associated with that type of a house depend on the energy source used to supply the house with electricity. However, the energy efficient house or a traditional house heated with natural gas generates gases other than CO2.

Of particular interest to rural homeowners is the use of biomass in the form of wood pellet. Compared to coal, a kilogram of wood pellet emits 22.5% less NO2 [64]. Finally, wood pellet emits 96% less particulate matter than coal. The actual differences in GHG emission reduction depend on the energy-efficiency of the specific model of the boiler. The use of natural gas, infrequent in rural areas, also substantially reduces GHG emissions. Heating with the gas condensing natural gas boiler emits 0.001% of SO2 as compared to a coal-fired boiler and virtually no particulate matter [65].

#### **5. Discussion**

The desire to own a family home will drive Poland's potential homeowners to build their new house, primarily in rural areas due to space availability. The calculated building costs (Table 1) must be considered in the context of the household ability to finance the construction. The cost difference between the single-family home built using the conventional technological solutions and the energy-neutral house is much larger than similar differences reported in studies in other countries. For example, the cost difference between the standard house built in accordance with Belgian regulations and a low-energy house (roughly comparable to the energy-efficient house considered here) was 4% and a passive (energy neutral) house 16%, respectively [66], while the difference between a standard and a passive house amounted to 10% in Germany [67]. Both studies suggest a considerably smaller relative differences between a conventional house and its energy-efficient alternatives established in the scenarios considered in the current study.

Mortgage financing has a bad reputation in Poland since the financial crisis of 2009–2010 because although the continuing GDP growth contrasted with the global malaise, many families suffered [68]. Prior to the financial crisis, banks offered mortgages priced in Swiss francs. The Swiss franc rapidly appreciated during the crisis and dramatically increased the mortgage debt servicing. The repercussions of that phenomenon are still felt today. Such recent memories combined with the shock in the ongoing COVID-19 pandemic and the induced economic slowdown affect households' attitudes discouraging the long-term credit-financed investment.

A number of future homeowners may not qualify for mortgage financing given the average income in rural households in general. Regional income disparities also persist. The lack of access to mortgage financing suggests the already observed prolonged construction because a rural households and their owners is likely to accumulate savings and then use them to finance the construction in stages. For example, the first stage will be limited to the unfinished house without the external wall insulation, windows and door. The last stage could involve the central heating system installation. An extended construction potentially delays the occurrence of gains to the household and the environment.

The large initial investment in the energy neutral house is expected to eliminate the cost of energy purchase to heat space and water. Those costs account of the about 60–70% of the cost of total energy purchase by an average household in Poland. The share is much larger than in many other EU countries or in the United States. An earlier study showed that the motive to save on the monthly energy bill was a major motive in the rural household investment in the thermal solar panels [8]. However, the rural homeowners mentioned the cost of RE utilizing equipment as a constrained and viewed the subsidy as important.

The currently operating furnace replacement program offers subsidies for qualifying households if they choose to replace their old furnace or boiler. The subsidy is matched to per capita income in the household and is proportionally larger for those with least income. The program aims at the improvement of energy-efficiency by households and includes, besides furnace replacement, subsidies to window and door replacement as well as external wall and roof insulation. The program also offers low interest loans for those who do not qualify for a grant. The program is more attractive to retrofitting an existing single-family home and less to future homeowners.

The comparison of the three construction technologies and the use of various insulating materials provides important knowledge to consider by future homeowners, but also by the owners of the existing rural family homes. Splitting the various construction stages (Table 1) demonstrates the costs associated with the use of alternative insulation. A retrofit of an existing house could involve specific projects. Among the largest is the resignation from a fossil-fuel based space and water heating system and its replacement by the RE-based installation such as wood pellet furnace. A retrofit generates potentially substantial savings to the household and the largest environmental benefits. The reduction of CO2 emission contributes to the national and EU climate policy and represents a direct contribution from households. The energy neutral house eliminates the emissions of NO2, SO2 and particulate matter comparison to the coal-heated single-family dwelling. The obtained energy savings, environmental

benefits, and economic gains support the conclusion that the thermo-modernization of a single-family rural residence and the modernization of the heating source bring direct economic gains to a household, indirect benefits in the local air quality improvement, and long term benefits in health benefits and environmental quality.

#### *Limitations of the Study*

The building model considered in this study assumes a simple floor plan and is limited in size. Many newly constructed houses are larger and there is a great variety of floor plans. Variation in the age, construction technology, and floor plans pose a challenge for thermal modernization of existing rural family homes. Although some parts of the house may be thermally insulated-for example the roof - other parts, like the internal partitions, would have to be rebuilt creating a domino effect forcing the renovation of several areas of the house. Improving the thermal insulation between various floors excludes portions of the house from use for the duration of the project. As a result, the thermal insulation project for an older house may cost more than a similar project for a new house.

The analyzed construction costs use the price lists reflect the asking price of local suppliers. Ultimately, prices of all building materials and heating systems may be both negotiable and region-specific. Consequently, the actual construction costs are likely to deviate somewhat from those considered here. Another source of price variation are changes in price level, which may have been affected by the ongoing pandemic and altered market conditions.

Another source of limitations is the use of energy prices from the publicly available sources and for a particular time period. Those prices will change over time and some, for example wood pellet or coal prices, will vary with respect to fuel quality which is ultimately decided by the homeowner. Homeowner choices of the heating energy type will influence the volume of GHG emissions, but will not reverse the general trend in emission reduction due to the shift away from using coal.

The current study did not account for the cost of the parcel, which is likely to be highly variable across rural areas in the country. A closer examination of household incomes and construction costs will enable the forecasting of new house construction, and potentially, regional economic growth and real estate market development. Earlier studies linked the energy performance of dwellings with market valuation [69,70] once the suitable data became available. Also, the scenarios presented in the current paper can be supplemented by financial analyses, which can account for regional household income variation and different types of mortgage loans. Finally, as new construction technology and heating systems become available, a future study will be necessary to update the data on energy savings and environmental benefits.

#### **6. Conclusions**

Improved energy-efficiency of single-family homes in Poland is required by the new building regulations, while the retrofitting of the existing detached houses in supported by government programs aiming at enhancing air quality as well as energy and climate policies. The energy demand reduction and environmental benefits occur primarily in rural areas because the detached houses dominate the rural landscape. Results from this study show that switching away from coal, still the primary energy sources in rural areas, required by new construction regulations leads to a substantial increase in construction costs of the shell of a house and even larger costs in installing the RE utilizing equipment to heat space and domestic water. The costs increase for a model single-family home considered here far exceed the previous estimates for other EU countries. The increased costs may deny the opportunity of residing in own home to many rural households due to debt servicing. However, retrofitting an existing house improving its energy-efficiency also generates substantial savings to households on their energy bill. Retrofitting split into smaller projects, as permitted by household savings, is realistic and likely to continue in rural areas.

The use of RE is required in all new detached housing, but encouraged in the existing single-family homes by the subsidy programs. The scenarios considered in this study used wood pellet, solar radiation

and geothermal energy. For rural households, the RE in the form of wood pellet may be more appealing because it can reliably heat the house, while being locally accessible. The intensity of solar radiation is inversely related to the heating energy needs of households in Poland determined by climate. The regional variations in climate will have to be considered by households investing in a new home since the winter temperatures can substantially vary. Regional considerations are often ignored in the "one-size-fits-all" regulations, and are verified by the actual site-specific conditions.

The environmental sustainability is well served by the new construction regulations and the scenarios considered in this study. Gains in reduction of CO2 are impressive once a household uses any amount of RE in comparison to a traditional rural house the uses coal. Retrofitting the existing house with the wood pellet burning boiler nearly eliminates CO2 and SO2 emissions, while substantially lowers other noxious gases and particulate matter. The effects are instantaneous in improving local air quality, while the broader effects benefit the implementation of the decarbonization efforts and help achieve goals of the climate policy.

**Author Contributions:** Conceptualization: A.S. and A.M.K.; Data curation: A.S.; Formal analysis: A.S.; Investigation: A.S., A.M.K. and W.J.F.; Methodology: A.S., A.M.K. and W.J.F.; Supervision: A.M.K.; Writing—original draft: A.S., A.M.K. and W.J.F.; Writing—review & editing: A.S., A.M.K., W.J.F., P.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

Construction materials used in the various scenarios were selected by the architectural design firm Archiko, Kornelia Lisowska-Siudek, Manager, and the labor costs were estimated by the construction company collaborating with the design firm. Construction cost estimates were also based on the estimations developed by the company, "Dobredomy".

#### **Appendix B**


**Table A1.** Annual heating energy needs, energy production costs, auxiliary electricity needs and total costs for a rural detached residence central heating system.

Source: Based prices listed by [71] and data from [16]. <sup>a</sup> Price from [16].


**Table A2.** Energy generation costs for the domestic water heating system.

Source: Calculations based on company price list [71], and data from [16].

#### **References**


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© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## *Article* **Energy Efficiency of Maize Production Technology: Evidence from Polish Farms**

#### **Anita Konieczna 1, Kamil Roman 2,\*, Monika Roman 3, Damian Sliwi ´ ´ nski <sup>4</sup> and Michał Roman <sup>3</sup>**


**Abstract:** The purpose of this work is to determine the impact of selected silage maize cultivation technologies, including energy inputs in the production chain (cultivation, harvesting, heap placing), on energy efficiency. The analysis of energy inputs, energy efficiency for the silage maize production technology were estimated. The research was performed for 13 farms producing silage maize. The data from the farms covered all the activities and the agrotechnical measures performed. The calculations of energy inputs made for the silage maize production for selected technologies were performed using the method developed by the Institute of Construction, Mechanization and Electrification for Agriculture (IBMER), once the method was verified and adapted to the needs and conditions of own research. Based on the accumulated energy production and the energy accumulated in the yield, energy efficiency index values for 13 silage maize cultivation technologies were calculated. The greatest impact on the results of energy efficiency calculations was shared by fertilizer and fuel inputs. In conclusion, it can be stated that, in terms of energy efficiency, maize cultivation is justified and it can generate energy benefits.

**Keywords:** energy efficiency; energy accumulated; crop production; silage maize; biomass; farms

#### **1. Introduction**

Crop cultivation is of special importance for covering the demand for consumption and animal feed and, to a growing extent, also for energy [1]. The socioeconomic progress, scientific and technical advancements, and hence the economic development result in an ongoing increase in electricity and transport fuel consumption globally, which triggers an increase in the concentration of pollution and environmental degradation (water, soil, air) [2–4]. The danger that is associated with this matter is the continued increase of unemployment and famine factors, unless intensive and preventive tasks are introduced, especially in saving agro-systems transformation. Past actions were based on previous generations' experience. The increased level of development in various fields requires us to use the results of interdisciplinary research. At the same time, the rapid changes in the conditions of the agro-systems environment and the growing demand for new, more effective technologies require the simultaneous contribution of knowledge not only in the field of food production, but also in the field of the quality of newly created products, their marketing and maintaining the ethical principles of their acquisition, processing and distribution. In particular, it is about the links between the humanities, technical, and agronomic sciences, creating an interdisciplinary consilium of experts on the transformation of agro-systems.

Considerations on the effectiveness of agro-systems transformations, both in the past and in the long-term perspective, lead to the knowledge and explanation of the

**Citation:** Konieczna, A.; Roman, K.; Roman, M.; Sliwi ´ ´ nski, D.; Roman, M. Energy Efficiency of Maize Production Technology: Evidence from Polish Farms. *Energies* **2021**, *14*, 170. https://doi.org/10.3390/en1401 0170

Received: 25 November 2020 Accepted: 25 December 2020 Published: 31 December 2020

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mechanisms of influence of various environmental factors on the anticipated changes in the parameters describing the energy-technological condition of the considered objects. The development of agriculture in line with the paradigm of sustainable development has become particularly important for industrialized countries, which previously based the development of the agricultural sector on the industrial model. Especially in Europe, the agricultural model based on too intensive fertilization, mechanization, and concentration led to the deterioration of the quality of the natural environment (Stoate et al., 2009). One of the elements of this research from the microeconomic perspective (at the farm level) is the comprehensive energy consumption estimated with the so-called pull method was started at the end of the 1970s [5]. In Europe, the precursor of such research was Pellizzi [6,7]. The energy performance indicators of respective means of production sometimes differ quite considerably depending on the authors, the indicator value changes with time, which is due to the changes in industrial production methods, more complete and more accurate energy consumption research in various areas of human life and production activity [5,8]. The energy technology method to assess the food economy transformation effectiveness was proposed by Nowacki [9], presenting the reasons for the macroeconomic approach to the food system. He covers the problem of the relationships of the economic measures, energy and sociological management effectiveness. As the technological level of an agricultural facility grows, human labor inputs decrease, which is the cause of a significant outflow of people employed in agriculture to other professions.

In the microeconomic approach, the research of the accumulated production energy consumption and efficiency is performed to evaluate the management quality in the enterprise, including an agricultural farm. The evaluation of the outcomes and economic methods management effectiveness often fails as the prices imposed often do not correspond to the cash value of the goods or energy offered. Hence, an increase in the importance of the evaluation of the energy consumption and energy efficiency method based on the values expressed in reference energy units is seen, allowing for their comparison irrespective of the place, time, and price relations. Bearing in mind the preferences, payments and grants for agricultural production, consumables and raw materials, services and credits applied on the EU market, the study of the energy efficiency becomes of special importance even though it will not replace the economic analyses which, under more complete market economy conditions, are definitely the best and the simplest business activity evaluation methods [10].

Because of a growing energy consumption, new methods of energy generation are searched for, the existing ones are improved, and the participation of the renewable resources of energy is increased in the energy balance. The rational use of the resources is nowadays associated with a permanent use of renewable resources, which means using them in such amounts in which their increase occurs [11]. The topic of renewable energy resources is one of the many aspects referring to the limited resources of fossil fuels, a considerable share of the energy sector in the greenhouse gas (GHG) emissions, which contributes to climate changes and an increase in energy security. In 2007 the Member States accepted the so-called climate and energy package, the assumptions of which are, e.g., limiting the GHG emissions and enhancing the energy security. One of the ways to accomplish those objectives is to increase the share of energy from the renewable resources in its total consumption. A special attention must be given to, e.g., agricultural biogas, the gas produced in the process of methane fermentation of agricultural raw materials, agricultural by-products, liquid or solid animal feces, by-products, waste or the remains from the processing of the products of agricultural origin or forest biomass of plant biomass collected from the areas other than recorded as agricultural or forest, except for the biogas produced from materials derived from sewage treatment plants and landfill sites [12]. That renewable energy is considered to show a potential as it is a stable and predictable source (important in terms of energy security) meeting a number of positive functions not only for the electrical power system, next to the energy and economic benefits as well as the environmental ones; it decreases the GHG emissions and provides global and local social

benefits. It helps activate the rural areas, it creates new jobs, it enhances the investment attractiveness of the region [13–15].

In Poland, to produce the agricultural biogas, most frequently a mixture of animal feces with energy crops or with by-products of agricultural origin is used. Applying co-substrate with a higher content of dry weight, as compared with its content in animal feces, enhances the production of biogas and the process economic effectiveness [16]. The right combination is conditioned by the biogas potential of each component as well as component interaction [17]. An excellent supplement to the fermentation mass in terms of technology is, e.g., maize silage. Literature provides many reports on the use of maize for energy purposes or [18–20] the relations between the maize prices and the prices of energy materials [21,22].

One must note, however, that growing maize requires high energy inputs, hence a need to perform research to increase the production energy efficiency. Energy efficiency must be defined as a ratio of the energy value of the biomass yield (the accumulated energy contained in biomass) to the total energy inputs (the accumulated energy required to produce the biomass) [10,23]. The cultivation technologies applied affect the environment to a varied extent and so, in terms of maize cultivation for energy purposes, calculating the energy efficiency becomes essential [24]. Estimating the energy consumption and energy efficiency of agricultural materials is indispensable for energy crops in a form of renewable energy. According to the requirements of Directive 2009/28/WE (RED) on the promotion of the use of energy from renewable sources, processors of biofuels are required to prove that the production from agricultural raw materials and the whole process of liquid biofuels production meet the sustainability criteria. It can be demonstrated by the LCA (life cycle assessment) method, that proves the reduction of GHG emissions along the entire chain of production. [25,26]. Those considerations are based on analyses at the microeconomic level concerning only the energy efficiency of various technologies of the maize for silage production in use of raw material for processing into biogas as an energy carrier. The analysis of the drag method by Pellizzi, adapted to Polish conditions by Wójcicki, allows the comparison of results whether the place, time, and price relationship was used. The drag method is successfully used by many authors and institutions, for example IBMER-ITP. The biomass production efficiency is very important in terms of increasing its share in the energy production. Because of the fears of a competition between plant production for energy purposes and crops for human consumption, actions are taken and research is performed to decrease the energy consumption of plant production owing to the optimal planning and possibly the most effective use of the land allocated for cultivation, compliant with the principles of sustainable development. An example of such research can be found, e.g., in the reports by Houshzyara et al. [27,28].

The primary objective of the paper is to determine the energy efficiency of maize with the use of various technologies. With that in mind, the analysis of energy inputs was made and energy efficiency was calculated for the silage maize production technology. To achieve the objective, the results of own research performed on 13 agricultural farms were applied.

The respective sections of the article present the theoretical grounds for the use of maize for energy purposes followed by the experimental part of the energy efficiency analysis. After the introduction, chapter 2 discusses in detail the source material and research methods. The third chapter covers the results of the analyses. With the basic information on the maize market, calculations were made on accumulated energy, the structure of energy inputs and energy efficiency. The last part of the article provides discussion and results.

#### **2. Maize as an Energy Crop**

Maize, similarly as most energy crops, is mostly used as a starch material derived from seed and as a material mostly for producing bioethanol [29] and as biomass including leaves, stems, and blade apex. Biomass can be used to produce bioethanol of the second generation, for incineration [30–32] or as silage for biogas production [33,34]. Interestingly,

the maize acreage in Poland since 2007 has increased by 146% and in 2018 it was 0.65 m ha (Figure 1), which accounted for 8% of the total acreage of crops in the EU.

Silage maize is a roughage used for cattle feeding. In fact, for the entire year of the fodder crop field harvest structure, maize shows the highest acreage, in 2016 the green maize yields accounted for 73.5% of the total fodder crop yields (Figure 2). Next to the silage allocation to animal feed for livestock or milk production, there also appeared a possibility of using it for energy purposes, as a valuable substrate for methane fermentation bacteria for biogas production. One of the most frequently applied substrates of agricultural origin is slurry, with varied properties depending on the feeding method or the animal species. A relatively low content of dry weight requires supplementation with substrates, e.g., plant substrates. An excellent supplement for the fermentation mass in terms of technology is maize silage which, according to the National Centre for Agriculture Support (KOWR), in 2018, accounted for 12% of the total substrates used in agricultural biogas plants (Figure 3).

**Figure 2.** Structure of field green fodder crops production (Total production of field green fodder crops = 100. Source: own elaboration based on [36].

The key criterion of the applicability of maize silage for biogas production is the share of dry weight from 28 to 35%, to much extent dependent on the right harvest date [38]. One of the biggest assets of maize is its high yields (photosynthesis C4); most frequently from 30 to 50 t/ha. For comparison, the average rye yield under Poland's conditions is 2.8 t per hectare, wheat—4.7 t per hectare for winter wheat and 3.6 t per hectare for spring wheat; the 2011–2015 means. One hectare of silage maize can produce from 4050 to 6750 m3 of biogas, which can generate from 87 to 145 GJ of energy (Table 1). Biogas production from 1 ton of silage can reach 200 m3, and from 1 ton of dry weight of silage the average of 550–650 m3 of biogas is produced. The amount of methane production ranges from 300 to 400 m3 per ton of dry silage weight [39–41].


**Table 1.** Average yields, biogas production, and energy from silage maize.

Source: own elaboration based on [42,43].

Maize shows, e.g., high yields of green weight per area unit, a high biogas yield, a good ensilaging capacity [44]. The productivity of photosynthesis in C4 plants is about 1.5–2 times higher than in C3 plants; hence a high interest in those plants to be used for energy purposes [45–48]. The foreseen climate changes have and will have a high impact on crop cultivation conditions. Maize must be definitely considered a species which because of its physiology, gets fast adapted to unfavorable climate changes [49]. Table 2 presents the maize adaptation to climate changes.

The maize cultivation technologies, irrespective of the direction of use, should consider the economic effectiveness, energy efficiency as well as, while facing climate changes, the environmental effectiveness, to alleviate and to decrease the rate of environmental changes, and to limit the GHG emissions [50]. Accomplishing those goals is considered feasible owing to lowering the energy consumption of production technologies and increasing the efficiency. The plant production, including the production of silage maize, requires performing many agrotechnical treatments. The factors affecting the silage maize cultivation success, acquiring a high-quality material in terms of its ensilaging applicability are the adequate agrotechnical practices, the cultivar selection adequate for the climate zone

and the stand [39,51]. The basic principles for making maize silage are accurate crushing, adding a preservative, fast placing of the heap or filling the silo, hermetic coverage, and the adequate pick-up, which affects the silage quality and limiting losses [52]. As for an inaccurate packing, the remaining oxygen makes ensilaging longer, it can lead to the development of undesired aerobic microorganisms. The hermetic coverage of the heap with foil prevents from the rainwater penetrating into the silage, and the load—the right ensilage straw deposition [53–55]. The tillage system and the dependent material and energy inputs, the frequency of practices, the dates of the agrotechnical practices performed, the harvest at the optimal date with a minimum level of losses during the practices are the key factors of the production energy efficiency.

**Table 2.** Maize adaptation and response to an anticipated climate change.


Source: own elaboration based on [49].

#### **3. Materials and Methods**

#### *3.1. Source Material and Object Characteristics*

The quality of agricultural enterprise management can be estimated with a balance sheet for the production period, breaking down the revenues and inputs and the incomes which can be expressed in a cash unit or the balance sheet for the business activity can be developed by breaking down the inputs and incomes in reference energy units (MJ) and reference grain units (JZ) [10]. The efficiency is a quotient of the outcome to the input [56].

The research was performed on 13 farms in the Podlaskie voivodeship (in southeastern Poland). The climate of this region is moderate with huge continental influence. This voivodeship is dominated by agriculture, which is the main branch of the region's economy. The fodder area is approx. 55% of the agricultural area. Over 31% stands for sown area on the arable land are fodder plants. In the studied farms the silage maize was grown in real farming conditions. The crop acreage ranged from 2.0 to 13.0 ha. The fields were 0.05 to 2.5 km away from the habitation. The yields varied and ranged from 45 to 80 t·ha−<sup>1</sup> (Table 3).


**Table 3.** Selected elements characteristic for silage maize cultivation.

Source: own study.

The data from the agricultural farms on selected silage maize cultivation technologies were provided in the elaborations and process sheets, breaking down all the factors and agrotechnical practices (record of treatments and practices as well as production inputs), especially:


With the method of direct interview with farmers, made twice over the vegetation period, there were determined the levels of the agrotechnical factors applied, which provided the data on the means-of-production inputs for the technologies investigated, following the silage maize cultivation technologies applied on a given farm and the consumption of the real sowing material, natural and artificial fertilizers, plant protection agents, and the yields per hectare.

The selected agricultural farms varied in terms of the type and amount of the fertilization applied. As for 12 out of 13 cultivation variants, natural fertilization was involved in 6 variants—manure only (no 3, 4, 9, 10, 11, 13) at the doses from 12.5 t·ha−<sup>1</sup> (no 13) to 41.4 t·ha−<sup>1</sup> (no 4), in 1—only slurry at the dose of 20 t·ha−<sup>1</sup> (no 6), in 5—manure and slurry (no 1, 2, 5, 8, 12), manure—from 30 (no 8, 12) to 47.1 t·ha−<sup>1</sup> (no 1) and slurry—from 14 t·ha−<sup>1</sup> (no 5, 8) to 20 t·ha−<sup>1</sup> (no 12), respectively. The cultivation technology marked with number 7 did not involve natural fertilization, whereas technology 7—used mineral fertilization only.

In the objects under study there were also considerable differences in the tractors and machinery used. The tractors engaged in agrotechnical practices and actions, harvest, technology transport, or placing a heap varied in terms of power and weight. Depending on the type of the work performed, carrying out the actions with own tractors, machinery, and tools or outsourced as services, the power and weight of tractors ranged from 22.4 kW for Ursus C330 of 1675 kg to 114 kW U1634 with 5190 kg. The harvest was made using the aggregate of a tractor with a tractor-operated chaff cutter (8 plantations) and with forage harvesters (5 plantations) with the power of up to 300 kW. For forage kneading and placing a heap of silage, tractors with weight added reaching the weight of up to 6 tons (ZT 232A) were used.

#### *3.2. Silage Maize Production Energy Consumption*

The energy inputs for silage maize production for selected technologies were calculated with the calculation method developed by IBMER [57,58] following a verification and adapting it to the needs and conditions of own research. The accumulated energy consumption stands for the total consumables and raw material and energy inputs in silage maize production technologies. To calculate it, the following dependence was used:

$$\mathbf{E\_{pro}} = \sum \mathbf{E\_{mat}} + \sum \mathbf{E\_{tm}} + \sum \mathbf{E\_{ON}} + \sum \mathbf{E\_{l\prime}} \tag{1}$$

where, Epro—the sum of energy inputs incurred on the silage maize production, [MJ·ha<sup>−</sup>1], Emat—energy consumption of consumables and raw materials engaged in production, [MJ·ha−1], Etm—energy consumption of tractors, machinery, and tools [MJ·ha−1], EON energy consumption of fuel, [MJ·ha−1], El—energy consumption generated by human labor, [MJ·ha<sup>−</sup>1].

The total value of accumulated energy consumption includes: energy consumption of consumables and raw materials engaged in production, energy consumption of the use of tractors, machinery and tools, the energy consumption of the fuel, and the energy consumption of the labor. The respective components of accumulated production energy consumption were calculated following the formulae: (Emat, Etm, EON, El).

Energy consumption of materials involved in production:

$$\mathbf{E\_{mat}} = \mathbf{E\_s} + \mathbf{E\_f} + \mathbf{E\_{pch'}} \tag{2}$$

where, Es = Ms ∗ Is—energy contained in the seeds of maize, [MJ·ha<sup>−</sup>1], Ms—seed weight, [kg·ha−1], Is—unit energy consumption index of maize seeds, [MJ·kg−1], Ef—energy contained in fertilizers, [MJ·ha<sup>−</sup>1],

$$\mathbf{E\_{f}} = \mathbf{E\_{nf}} + \mathbf{E\_{mf}} \tag{3}$$

where, Enf—energy contained in natural fertilizers, [MJ·ha<sup>−</sup>1],

$$\mathbf{E\_{nf}} = \mathbf{E\_{nfm}} + \mathbf{E\_{nfs}} \tag{4}$$

where:

Enfm = Mnfm ∗ Infm—energy contained in manure, [MJ·ha<sup>−</sup>1], Mnfm—manure mass, [kg·ha<sup>−</sup>1], Infm—unit manure energy consumption index, [MJ·kg<sup>−</sup>1], Enfs = Mnfs ∗ Infs—the energy contained in the slurry, [MJ·ha<sup>−</sup>1], Mnfs—slurry mass, [kg·ha<sup>−</sup>1], Infs—unit energy consumption index of slurry, [MJ·kg<sup>−</sup>1], Emf—energy contained in mineral fertilizers, [MJ·ha<sup>−</sup>1],

$$\mathbf{E\_{mf}} = \mathbf{E\_{mfN}} + \mathbf{E\_{mfP}} + \mathbf{E\_{mfK}} + \mathbf{E\_{mfCa}} \tag{5}$$

where:

EmfN = MmfN ∗ ImfN—energy contained in nitrogen fertilizers, [MJ·ha<sup>−</sup>1], MmfN—mass of nitrogen fertilizer, [kg·ha<sup>−</sup>1], ImfN—unit energy consumption index of nitrogen fertilizers, [MJ·kg<sup>−</sup>1], EmfP = MmfP ∗ ImfP—energy contained in phosphorus fertilizers, [MJ·ha<sup>−</sup>1],

MmfP—mass of phosphorus fertilizer, [kg·ha<sup>−</sup>1], ImfP—unit energy consumption index of phosphorus fertilizers, [MJ·kg<sup>−</sup>1], EmfK = MmfK ∗ ImfK—energy contained in potash fertilizers, [MJ·ha<sup>−</sup>1], MmfK—mass of potash fertilizer, [kg·ha<sup>−</sup>1],

ImfK—unit energy consumption index of potash fertilizers, [MJ·kg<sup>−</sup>1],

EmfCa = MmfCa ∗ ImfCa—energy contained in calcium fertilizers, [MJ·ha<sup>−</sup>1], MmfCa—mass of calcium fertilizer, [kg·ha<sup>−</sup>1],

ImfCa—unit energy consumption index of calcium fertilizers, [MJ·kg<sup>−</sup>1], Epch = Mpch ∗ Ipch—energy contained in plant protection chemicals, [MJ·ha<sup>−</sup>1], Mpch—mass of plant protection chemicals, [kg·ha−1],

Ipch—unit energy consumption index of plant protection chemicals, [MJ·kg<sup>−</sup>1],

Energy consumption of using tractors and machines was calculated according to the formula:

$$\mathbf{E\_{trn}} = \mathbf{E\_t} + \mathbf{E\_{rn}} \tag{6}$$

where, Et—energy consumption of tractors, [MJ·ha−1], Em—energy consumption of the machine/s, [MJ·ha<sup>−</sup>1].

$$\mathbf{E\_t} = \frac{\mathbf{M\_{t}} \ast \mathbf{I\_{t}} + \mathbf{M\_{sp}} \ast \mathbf{I\_{sp}}}{\mathbf{I\_{Pect}} \ast \mathbf{I\_{oc}}},\tag{7}$$

where:

Mt—mass of the tractor, [kg],

It—unit tractor energy consumption index, [MJ·kg<sup>−</sup>1],

Msp—mass of worn spare parts on the tractor, [kg],

Isp—unit index of energy consumption of spare parts, [MJ·kg<sup>−</sup>1],

Ipet—exploitation potential (standard number of hours of operation of the tractor during its use, [h]

Ioe—operational efficiency of the machine when performing a given procedure, [ha·h<sup>−</sup>1].

$$\mathbf{E\_m} = \frac{\mathbf{M\_m} \ast \mathbf{I\_m} + \mathbf{M\_{SP}} \ast \mathbf{I\_{SP}}}{\mathbf{I\_{Pem}} \ast \mathbf{I\_{oc}}},\tag{8}$$

where:

Mm—machine weight, [kg],

Im—unit energy consumption index of the machine, [MJ·kg<sup>−</sup>1],

Msp—mass of used spare parts in the machine, [kg],

Ipem—exploitation potential (standard number of hours of operation of the machine during its use, [h]

Energy intensity brought in the form of human labor:

$$\mathbf{E}\_{\rm l} = \frac{\mathbf{N}\_{\rm t} \ast \mathbf{I}\_{\rm to}}{\mathbf{I}\_{\rm oc}},\tag{9}$$

where, Nt—number of employed tractor drivers, machine operators, Ito—unit index of energy consumption of work by tractor driver, machine operator, [MJ·rbh<sup>−</sup>1],

Energy consumption of used fuel:

$$\mathbf{E\_{ON}} = \mathbf{Z\_{ON}} \cdot \mathbf{I\_{ON'}} \tag{10}$$

where, CON—fuel (diesel) consumption of tractors and self-propelled machines, [dm3·ha<sup>−</sup>1], ION—unit fuel energy consumption index, [MJ·kg<sup>−</sup>1].

Formulas (1)–(10) include unitary indicators of energy consumption, assuming different values reported by respective authors, which is related to the changes in the production methods, living standards, etc. To calculate the energy inputs related to direct energy carriers, mineral fertilizers, agrochemicals, the application of tractors, machinery, and human labor, the accumulated energy consumption indicators for respective energy resources were used (Table 4) [5,10,59–61].


**Table 4.** Unitary energy consumption indicators.

Source: own elaboration based on [5,30,60–62].

The unitary energy consumption indicators used for the calculations express the energy equivalent of the unit of given means of production engaged in the production for a given silage maize cultivation technology, e.g., 1 kg of tractor or machinery, 1 kg of the raw materials used, 1 dm3 of fuel and 1 man-hour of human labor.

#### *3.3. Silage Maize Production Energy Efficiency*

Energy efficiency of the production of specific yield must be considered a ratio of the energy value of the product to the amount of energy consumed for the production. The energy efficiency index value was calculated following the dependence provided by Harasim [63], Ku´s [64] and expressed as a dimensionless coefficient:

$$\mathbf{E}\mathbf{p} = \mathbf{E}\mathbf{v}/\mathbf{E}\mathbf{p}\mathbf{r}\_{\prime} \tag{11}$$

where, Ep—energy efficiency index of silage maize, Ev—energy value of the maize yield per 1 ha, [MJ·ha−1], Epro—the sum of energy inputs incurred on the silage maize production, [MJ·ha<sup>−</sup>1].

Using the unitary energy index for silage maize, 0.8 MJ·kg−<sup>1</sup> [29], the value of the energy of the maize yield was calculated from the dependence:

$$\mathbf{E}\mathbf{v} = \mathbf{I}\_{\text{maizze}} \cdot \mathbf{Y}\_{\text{m}\prime} \tag{12}$$

where, Imaize—unit energy index maize silage, [MJ·kg−1], Ym—maize yield for silage, [kg·ha<sup>−</sup>1].

The estimates of the energy consumption for the production of respective crops, including silage maize, are applied to determine the energy consumption of respective kinds of agricultural biomass for energy use. The methodology for investigating the energy consumption of plant production, silage maize, is applied to study the outcomes and energy efficiency of agricultural material for human consumption or for energy purposes.

#### **4. Results**

#### *4.1. Accumulated Energy*

One of the elements affecting the energy consumption of the production process is the energy inputs resulting from the consumption of traditional energy carriers (ON) in the technologies applied. For that group of energy carriers and for the cultivation technologies analyzed and investigated, the diesel oil was considered.

The accumulated energy consumption of the energy carriers in a form of diesel oil for the technologies studied varied. In extreme cases the differences were more than double. The data provided in Figure 4 show that the accumulated energy consumption of energy carriers ranged from 4684.11 (technology 7) to 17,162.61 MJ·ha−<sup>1</sup> (technology 10). The average value of accumulated energy consumption of the energy carriers for the silage maize cultivation technologies was 10,561.90 MJ·ha<sup>−</sup>1.

**Figure 4.** Total energy for the diesel oil consumed [MJ·ha<sup>−</sup>1]. Source: own study.

The energy consumption for the cultivation technology is also affected by the accumulated energy related to the use of tractors, machinery, and tools for silage maize production (Figure 5). The calculations show that the share of the input of energy accumulated in tractors, machinery, and tools in the total accumulated energy ranged from 524.26 MJ·ha<sup>−</sup>1, which accounted for 1% (technology 1), to 12,196.21 MJ·ha−1, which accounted for 23.4% (technology 5). The mean value for the calculated accumulated energy consumption in tractors, machinery, and tools was 3886.28 MJ·ha−1. The share of energy generated by human labor is shown in Figure 6.

**Figure 5.** Total energy accumulated in the tractors, machines and tools used [MJ·ha−1]. Source: own study.

**Figure 6.** Amount of energy generated by human labor [MJ·ha<sup>−</sup>1]. Source: own study.

As for the silage maize cultivation technologies investigated, the energy input considered was the consumption of consumables and raw materials. The average value of the energy consumption accumulated in the consumables for the technologies analyzed was 21,051.22 MJ·ha−1. The share of the input of energy accumulated in the consumables in the total accumulated energy ranged from 3924.32 MJ·ha−1, which accounted for 32.1% (technology 13), to 31,845.65 MJ·ha<sup>−</sup>1, which accounted for 63.0% (technology 1) (Figure 7).

**Figure 7.** Total energy accumulated in the natural and mineral fertilizers, plant protection products, and seeds used [MJ·ha<sup>−</sup>1]. Source: own study.

#### *4.2. Energy Inputs Structure*

The structure of the energy consumption accumulated for technologies was calculated for respective energy inputs, namely the energy carriers, inputs of labor, the consumables, and raw materials as well as tractors, machinery, and tools. Figure 8 presents the share of accumulated material—energy inputs for the silage maize cultivation technologies from four energy inputs: in tractors, machinery, and tools, means of transport, as well as in the spare parts and materials used for the repairs of that equipment, the direct energy carrier, namely the diesel oil, the consumables, and raw materials used for production and the human labor inputs.

**Figure 8.** Energy intensity accumulated in the technologies of maize silage from individual energy inputs. [MJ·ha−1]. Source: own study.

Of all the energy inputs analyzed, the greatest share in the total accumulated energy consumption was recorded for the consumables and raw materials; from 32.1% for technology 13 to 73.5% for technology 3. On average the share of the energy accumulated in the consumables and raw materials accounted for 53.7%. A lower share in the total energy consumption was recorded for the energy inputs related to the use of diesel oil for agrotechnical practices and jobs in the production chain. The values ranged from 14.6% (technology 3) to 42.2% (technology 13). The mean share of energy accumulated in the energy carriers accounted for 27.5%. Another element affecting the total production energy consumption was the energy accumulated in tractors, machinery, and tools. The average share of inputs from that input accounted for 10.2%. As for the technologies analyzed, the energy consumption values for tractors, machinery, and tools ranged from 1.0% (technology 1) to 23.4% (technology 5). The lowest share in the total accumulated energy consumption for production was the energy accumulated in human labor. The share of inputs from that energy input ranged from 3.4% (technology 3) to 13.2% (technology 10), and the mean value accounted for 8.6%. Table A1 in the Appendix A provides the results of the calculations of the energy accumulated for all the inputs of energy for each silage maize technology studied. The percentage share of accumulated energy for each of the inputs for the technologies researched has also been given.

#### *4.3. Energy Efficiency*

Table 5 demonstrates the calculated index of energy efficiency calculated for 13 silage maize cultivation technologies. The values range from 0.95 (technology 3) to 2.94 (technology 13). As for 11 out of 13 silage maize-growing technologies, the index value was higher than 1. It means that for the biomass produced the value of the accumulated energy was higher than the energy in the energy inputs made.


**Table 5.** Energy expenditure in means of production, energy accumulated in yield, and energy efficiency index.

Source: own study.

The most favorable technological variant in terms of energy efficiency was variant 13 for which, assuming as the reference 100% to be the mean value of the inputs of energy accumulated in the consumables and raw materials (20,847.75 MJ·ha−1); the energy accumulated in that input was 81.2% lower than the average value, which had the greatest effect on the result despite a considerably low yield (45 t·ha<sup>−</sup>1). Assuming the mean value of the inputs of energy accumulated in tractors, machinery, and tools for the technologies to account for 100% (3886.28 MJ·ha<sup>−</sup>1), for technology 13 those inputs were 53.6% lower than the average. The analysis of the human labor inputs made for production demonstrated that, as for technology 13, they are 61% lower than the mean for the technologies investigated. As compared with the mean value of the inputs of energy accumulated in direct sources of energy; diesel oil (10,561.90 MJ·ha−1, assumed as 100%) for the technologies under study the inputs of the accumulated energy from that input in technology 13 were 51.1% lower than the mean value.

For two plantations of all those investigated, the energy efficiency index value was below 1, which means that the technological solutions applied for the crops marked 3 and 5 showed a lack of energy efficiency and the inputs of accumulated energy used to produce 1 biomass yield unit were higher than the energy accumulated in the yield, which was an unjustifiable solution in terms of energy. The greatest impact on the energy efficiency calculation results, 0.99 for technology 5, was recorded for the energy accumulated in tractors, machinery and tools, and for technology 3 (0.95), the inputs of energy accumulated in the consumables and raw materials.

#### **5. Discussion and Conclusions**

According to the union market preferences like grants and subsidies for agricultural production (materials, services, and loans) the studies in the field of energy efficiency have huge importance. Plant production, including silage maize, requires performing many agrotechnical practices. Currently there are undergoing analyses about the possibility of sustainability development use and the reduction of energy inputs to reduce the negative impact on the environment, water, soil, air. It can be afforded by the reduction of the number of treatments performed in order to, among others, replacing the conventional tillage system with simplified or direct sowing [25,65]. The tillage system and the resulting material and energy inputs, the frequency of jobs, the dates of agrotechnical practices, the harvest at the optimum date with a minimum level of losses while performing the jobs are the key factors of production energy efficiency [66].

The plant production energy efficiency has been researched by many authors who referred the index to single agrotechnical practices [67,68], tillage systems [69,70], the entire technologies and agricultural products [71–74], and to elements of crop rotation [67,70,75].

As for the research of silage maize production technologies in the structure of energy inputs made for production, it was found that the highest share is recorded for the inputs of energy accumulated in the consumables and raw materials; 53.7% on average, and for the energy input the highest share is noted for fertilizers (98% on average, assuming 100% mean energy accumulated in consumables and raw materials). Gorzelany et al. [76] and Budzy ´nski et al. [74] also claim that the highest share in the structure of the inputs of energy made for producing silage maize is accounted for the consumables and raw materials, 56%, respectively, and, depending on the technology traditional 76.5%, including fertilizers 71.5%, integrated, including fertilizers—63.8%. Similarly, as provided in the results of this research, the lowest share was found for the inputs of energy in a form of human labor, depending on the technology, 0.6 and 0.9%. The analysis of the results of the research of the technologies covered by these considerations also demonstrates the lowest share of energy of the human labor in the structure of inputs, on average 8.6%. It can be slightly higher for the results reported by the above authors as they did not consider the operation time of the tractors and machinery, and the time of driving to the field. In those analyses the time was factored in and considered essential in terms of performance of the sets of machinery and tools as well as fuel consumption. The inputs of accumulated energy from all the inputs for the technologies studied ranged from 12,233.01 to 52,229.49 MJ·ha–1, respectively for technologies 3 and 5; 38,735.83 MJ·ha–1 on average. Szempli ´nski and Dubis [77] generated 22,000–24,000 MJ·ha–1 of the energy inputs made for silage maize production technologies, Budzy ´nski et al. [74]—23,900—18,700 MJ·ha–1 depending on the input intensity, Gorzelany et al. [76] 24,305 MJ·ha–1, and considering the energy produced in the yield—37,800 MJ·ha–1; the energy efficiency index was 1.5. In the present study the mean result of energy efficiency for the silage maize production technologies was 1.44, the minimum value—1.04 (disregarding the technologies with no such efficiency), the maximum value—2.94. Wielogórska et al. [78] reported on the research performed to evaluate the silage maize-growing technologies on the farms with the agricultural land acreage of at least 5 ha, show that the mean energy inputs made for cultivation were 22,200 MJ·ha<sup>−</sup>1, and the technologies investigated recorded a high energy consumption index which was the crops mean of 3.2.

The research performed for 13 technologies for the plantation acreage from 2 to 13 has shown that the most favorable technology solution in terms of energy efficiency was variant 13. With the results in mind, it can be claimed that a relatively low natural fertilization and a lack of mineral fertilization decreased the maize silage production energy consumption (12,233.01 MJ·ha–1) enough for, despite the lowest yield for all the technologies, the highest value of the energy efficiency. Assuming that 100% is the mean value of the energy accumulated in the consumables and raw materials for those technologies (20,847.75 MJ·ha–1) in variant 13 the energy accumulated from that input was 81.2% lower than the mean value, which significantly affected the silage maize production energy efficiency index value. The energy accumulated in the yield for that technology was 28.1% lower than the mean value (50,054.15 MJ·ha–1). As for that technology, the value of the energy efficiency index was 182% higher than the value for the technology least favorable in terms of energy, however, with the index value above 0 (1.04 technology 12).

For two plantations of all those investigated, the energy efficiency index value was lower than 1 (0.95 and 0.99). It means that the technological solutions applied in the variants marked 3 and 5, respectively, recorded a lack of energy efficiency, the accumulated energy inputs made to produce a biomass yield unit were higher than the energy accumulated in the yield. A higher yielding and the accumulated energy of the yield, 40,000.00 MJ·ha–1 and 51,696.00 MJ·ha–1 (for technologies 3 and 5, respectively) did not compensate for the high energy inputs made for their accomplishment; 42,116.45 MJ·ha–1 for technology 3 and 52,229.49 MJ·ha–1 for technology 5. Those were the solutions which were unjustified in terms of energy.

When deciding on a given technological solution, it should be remembered that the increase in yields is not linear to the increase in energy inputs. Depending on this, there is a point to which increasing the level of expenditure is justified. Above the optimal value adjusted to e.g., type of crop, farm size, cultivation system, increasing energy inputs are not compensated in the yield.

In agricultural practice, due to a decrease in the inputs and a negative impact on the environment, various production technology modification methods, also for plant production, for simplified tillage systems and limiting the inputs are being searched for [79–81]. The energy calculation should be an essential element for the assessment of plant production, which is frequently limited to economic and production criteria [70,74].

To recapitulate, one can state that, in terms of energy efficiency, growing maize is justifiable and it can trigger energy benefits. The main factors dividing the cultivation technologies and harvesting maize for silage in typical Polish farm are size of plantation, diversity of crops (according to soil and climatic conditions), the level of farmer education, technical procedure and knowledge, machine park and machine services availability, possibility of products managing (using and demanding), types of activities in the adjacent areas, preferences and involvement of production units, and possibility of usage in energy production e.g., in biogas plants. The development of agricultural biogas plants contributes to the new jobs opportunities in rural areas, which enables the diversification of farmers income sources. According to KOWR (31 August 2017), there are nine agricultural biogas plants registered in the Podlaskie voivodeship. The adequately selected silage maize production technologies make it a very attractive crop in terms of energy, which becomes of special importance while facing the need of using the energy from renewable resources. The analyses covered the maize production technology variants: a field production stage, through harvest to the preparation of raw material for ensilaging, and placing a heap. The energy efficiency of a further use will depend on the processing technology applied in the biogas plant, which will be a continuation of the previous considerations and support the research results obtained at this stage. Other limitations resulting from the production of bioenergy from biomass should also be considered. The intensive cultivation of certain crops requires large areas of cultivation because of their future energy potential (so-called energy crops like maize). This is related to the acquisition of a significant amount of the plant and may be associated with the excessive use of fertilizers and other substances polluting the soil and water, and the reduction of areas for food production [82].

According to the EIA (Energy Information Administration) report from 2015, by the year of 2040 world energy consumption will increase by 56%, world energy consumption will increase by 56%, which will lead to an increase in the world CO2 emissions up to 46% [82]. According to above, the estimation of GHG emissions released into the atmosphere is an important issue, which is planned as a continuation of the present research.

Moreover, in the literature there are analyzes of the relationship between the prices of energy raw materials and the prices of grains and oils [83]. In connection with this, an interesting research issue will also be the analysis of the relationship between the prices of maize and the prices of energy resources.

**Author Contributions:** Conceptualization, A.K., K.R., M.R. (Monika Roman), D.S., M.R. (Michał ´ Roman); methodology, A.K., K.R., M.R. (Monika Roman), software, A.K., K.R., M.R. (Monika Roman), D.S., M.R. (Michał Roman); validation, A.K., K.R., M.R. (Monika Roman), D. ´ S., M.R. (Michał Roman); ´ formal analysis, A.K., K.R., M.R. (Monika Roman), D.S., M.R. (Michał Roman); resources, A.K., M.R. ´ (Monika Roman), K.R.; data curation, A.K.; writing—original draft preparation, A.K., K.R., M.R. (Monika Roman), D.S., M.R. (Michał Roman); writing—review and editing, A.K., M.R. (Monika ´ Roman); visualization, A.K., M.R. (Monika Roman); supervision, A.K., K.R., M.R. (Monika Roman), D.S., M.R. (Michał Roman); project administration, M.R. (Michał Roman); funding acquisition, M.R. ´ (Michał Roman), M.R. (Monika Roman) All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** Value of accumulated energy consumption and the percentage share of accumulated energy in individual energy inputs for the technologies studied.


Source: own study.

#### **References**


**Jakub Jasi ´nski 1,\*, Mariusz Kozakiewicz <sup>2</sup> and Maciej Sołtysik <sup>3</sup>**


**Abstract:** The strategies, plans and legislation on energy market development and decarbonization in the European Union (EU) developed in recent years, such as the directives implementing the package "Clean energy for all Europeans", aim at promoting not only renewable energy sources, but also new institutions that involve the development of local energy markets and a greater role for citizens in managing their own energy generation. At the same time, Poland remains the economy most dependent on coal and one of the largest air polluters in the EU. In order to minimize this problem and to meet the direction of energy development in the EU, Poland decided to establish, among other things, an energy cooperative. It is intended to fill the gap in the development of the civil dimension of energy on a local scale and at the same time improve efficiency in the use of the potential of renewable energy sources in rural areas. The authors of the paper seek to verify the extent to which this new institution, which is part of the idea of a local energy community, one of the driving forces for the implementation of the objectives and directions of development of "clean energy" set by the EU, has a chance to develop. The research took into account the characteristics of energy producers and consumers in rural areas, economic preferences provided for by law, relating to the functioning of an energy cooperative and the existing alternative solutions dedicated to prosumers. A dedicated mathematical model in the mixed integer programming technology was used to optimize the functioning of an energy cooperative, and more than 5000 simulations were carried out, with a typical optimization task performed as part of the research with about 50,000 variables. The conclusions and simulations make it possible to confirm the thesis that profitable energy cooperatives can be established in rural areas, with the objective of minimizing the sum of energy purchases from the distribution network and losses on the energy deposit (virtual network storage) (the energy deposit (or network deposit) should be understood as energy introduced to the grid during generation surpluses for its subsequent consumption, taking into account the discount factor).

**Keywords:** energy cooperatives; renewable energy sources; rural areas; renewable energy community; mixed integer programming

#### **1. Introduction**

Decentralization of large-scale energy, replacing it with pro-ecological, distributed generation sources and building the civil dimension of energy [1,2], are the objectives of the energy transformation in the European Union (EU) [3,4]. EU legislation does not impose a precise formula for achieving these objectives, providing the freedom to individual member states [5,6]. Building energy self-sufficiency at local level is possible on the basis of institutions called energy communities (EC) [7]. The first of these is the energy community defined in the REDII Directive (Renewable Energy Directive II) [8], focused on a renewable energy community (REC) [9]. The second form of activity is the citizens energy community (CEC) [10], introduced by the market directive [11]. Both these concepts

**Citation:** Jasi ´nski, J.; Kozakiewicz, M.; Sołtysik, M. Determinants of Energy Cooperatives' Development in Rural Areas—Evidence from Poland. *Energies* **2021**, *14*, 319. https://doi.org/10.3390/en14020319

Received: 13 December 2020 Accepted: 6 January 2021 Published: 8 January 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

serve the development of locally distributed energy, have a legal personality and are characterized by voluntary and open participation [12]. Their primary purpose is to provide economic and environmental benefits at local level. The main differences between the REC and the CEC [13] are that large and medium-sized enterprises are excluded from the CEC, whereas energy generation capacity must be available in an REC (it must be from a renewable energy source—RES) and there are requirements for investment [14].

The EU direction of energy market transformation has also been reflected in Polish law, where, similarly to the community provisions, two institutions were created that introduce the civil dimension of energy. These include energy clusters [15] and energy cooperatives—the latter, being the newest [16] form of support for distributed civil energy, are the subject of consideration by the authors of this paper. The activity of an energy cooperative may be conducted in the territory of a rural or urban-rural municipality or in no more than three such municipalities directly adjacent to each other. The energy cooperative relies on:


The provision of opportunities to build local energy communities [14,17] on the basis of cooperatives [18,19] can be very important especially in rural areas [20]—including by developing the "smart villages" concept [21,22]. This is where the greatest potential exists for the use of renewable energy sources (including biomass and biogas), including those of a waste nature that are part of the characteristics of a circular economy [23,24]. Unfortunately, although solutions concerning energy cooperatives have been in place in Poland since mid-2019, by the end of 2020 no energy cooperative had yet been established [25]. The considerations of different options for the cost-effectiveness of creating an energy cooperative as a Polish response to the development of local energy communities in the EU [26] are the core of the research presented in the paper. However, it should be noted that it is not the purpose of this paper to assess the extent to which the institution of an energy cooperative meets Poland's obligation with respect to REC and CEC.

The main objective of our paper is to check where generation (number, type and capacity of installed sources) and consumption (energy demand) configurations the energy cooperative will be a cost-effective solution for the potential members of cooperatives. Thus an attempt will be made to answer the question of the production and consumer structure and the number of members that will be necessary for a form of self-organization such as an energy cooperative to develop. All restrictions and conditions resulting directly from the Polish law will be taken into account, and an analysis of the cost-effectiveness of establishing cooperatives in relation to the preferences that apply to individual prosumers will be analyzed. According to the authors, due, among other things, to different preferences for individual prosumers [27] and energy cooperatives (the so-called 1:0.8 or 1:0.7 discount rate for individual prosumers (this is a type of support scheme for prosumers in Poland: owners of micro-installations (capacity up to 50 kW) are allowed to exchange the surplus of energy produced under favourable conditions for gaps in energy production. The ratio is 1 to 0.8 for capacity up to 10 kW and 1 to 0.7 in the case of micro-installations between 10 and 50 kW) [28]) [29] and 1:0.6 for energy cooperatives [16]), the establishment of a cooperative will not always be economically justified. All the models of cooperatives proposed and researched in this paper, likewise data concerning energy production and consumption in households which are potential members of cooperatives, are anonymized real data from rural areas of Poland.

Our paper fills in a literature gap in the following ways. Firstly, our research, based on real data, allowed us to create several model energy cooperatives taking into account the conditions for the establishment and development of these institutions in Poland. Owing to this, the authors do not limit themselves to examining one specific example, but seek to show the most optimal model of operation of the citizens' energy community, which includes the energy cooperative. Secondly, our paper fills a methodological gap [30], as the methodological approach presented can easily be used in relation to other distributed energy institutions (especially the various energy communities), which have been and will be established in Europe and on other continents. Thirdly, it is novel at the national level as it investigates a problem not yet explored for Poland.

The contribution of the paper to scientific literature consists in proving that the energy communities' efficiency may be considered using optimization models in the mixed-integer programming technology, which are a basic tool for operational research applicable to decision-support processes in all areas of the economy. The paper also contributes to policyrelated literature, because the economic effects of the functioning of energy communities are underestimated in decisions regarding the creation of such new institutions [30–32], where legal [33], administrative [34,35] and technological aspects prevail [36–38].

The paper is structured as follows. The second section provides the rules for the functioning of energy cooperatives and the main model assumptions arising from Polish law and those made by the authors. The next section describes the output data and their selection, as well as the mathematical optimization method used in the research process. Then the results are presented and discussed step by step. The final section presents the conclusions that have made it possible to conclude the entire research work and to present its universal character in relation to the methodology of examining the cost-effectiveness of creating institutions that are part of the idea of energy communities, which is gaining increasing recognition all over the world.

#### **2. The Background of Energy Cooperatives**

#### *2.1. Energy Cooperatives as a Response to Renewable Energy Community (REC) and Citizens Energy Community (CEC)*

As a country whose energy sector is still mainly dependent on coal [39], Poland is unlikely to meet the minimum 15% national target for renewable energy sources (RES) [40] in the final balance of energy consumption in 2020 (the European Union is committed to achieving 20% of energy from renewable sources in gross final energy consumption in 2020. All member states have individual targets to ensure that the EU plan is met. Poland has committed itself to achieving 15%. For comparison, Sweden has the highest level of commitment (49%) and Malta the lowest (10%) [41]) [42] (official EUROSTAT data will be available in 2022). One of the solutions enabling the acceleration of Poland's green transformation is the popularization of local energy communities and the resulting decentralization of energy. In 2019, the Polish government introduced the institution of an energy cooperative in order to meet the objectives of the EU directives on the development of RES and citizens' energy communities. Energy cooperatives are also intended to:


Rural areas make up more than 93% of Poland, with almost 40% of the country's population. The increase in energy demand in these areas, coupled with increased consumption by agriculture, is forcing rural people to use it more efficiently and politicians to develop energy security strategies for rural areas [43]. This is possible by creating a sustainable energy policy using renewable energy sources. Rural areas are largely associated with food production and processing, where agricultural holdings are important. They should now be seen on the one hand as energy users and on the other as producers of energy or final energy components based on renewable energy sources [44], including those concentrated and developed in energy cooperatives.

The definition of an energy cooperative appeared in Polish law during the amendment of the Act on Renewable Energy Sources. Pursuant to this, an energy cooperative is one within the meaning of the provisions of the Cooperative Law [45] and the Act on Farmers' Cooperatives [46] the object of which is to generate electricity, heat or biogas (exclusively for the auxiliaries of the energy cooperative and its members). Energy cooperatives may be established (conditions to be met cumulatively):


#### *2.2. The Functioning and Billing Rules in an Energy Cooperative*

Energy cooperatives operate on the basis of a prosumer system consisting of energy billing on the basis of "discounts". With the energy cooperative the energy seller (a licensed seller of a given type of energy, designated by the Energy Regulatory Office (https://www.ure.gov.pl/en) in a given area) accounts only for the difference between the amount of electricity fed into the power distribution network and the amount of electricity drawn from it for the auxiliaries of the cooperative (its members) in the ratio corrected by the quantitative coefficient 1 to 0.6 (in the case of prosumers, the coefficients 1 to 0.8 or 1 to 0.7 apply in Poland, depending on the system capacity). In other words, for one MWh of energy generated by the cooperative and not used at a given moment by its members, i.e., fed into the distribution network (the network in this situation operates as a deposit (storage) for energy unused by the cooperative), 0.6 MWh (600 kWh) of energy can be drawn from the distribution network. This may happen at any time during the billing period when the cooperative's generation sources do not cover the electricity demand generated by its members. This billing concerns electricity fed into and drawn from the distribution network by all electricity generators and consumers who are members of the energy cooperative. The same applies if heat or gas is the subject of the cooperative's operation.

Therefore, it is reasonable to assume that the more the members of the cooperative "synchronize" the amount of energy generated and consumed at any given time so as not to discharge energy surpluses into the network, the greater the economic effects of the energy cooperative will be. It can be said that in such a situation the distribution network will, in a way, only "protect" the internal energy economy of the cooperative. For this reason, the authors considered that the production mix should be optimized for the pre-set demand, with the minimization of energy not drawn from the network deposit and that additionally purchased from the network, resulting from possible shortages of energy generated inside the cooperative.

As a prosumer the energy cooperative operates in the power system under a comprehensive agreement with an external energy seller. This regulates both the distribution and sale of possible energy shortages to cooperatives. For an energy seller, an energy cooperative is a single, collective final consumer subject to single billing. For internal billing of an energy cooperative between its individual members, the seller indicates the amount of energy fed into and drawn from the network by its individual members. The cooperative accounts for them in accordance with internally accepted rules. The amount of unused energy remains to be taken (compensated) within a given billing period, which is usually 12 months from the last day of the month in which the surplus occurred. The energy cooperative does not pay the following fees for the amount of electricity thus billed:


These costs are covered by the energy seller as part of the value of energy at its disposal, i.e., 40% of the energy fed into the distribution network by the energy cooperative. (For the amount of electricity drawn from the network storage (with a coefficient of 0.6), the energy cooperative does not pay variable fees for the distribution service and does not pay the seller the billing fees. In addition, the RES, capacity and co-generation fee is not charged and collected for the amount of electricity generated in all RES systems of the energy cooperative, and subsequently consumed by all consumers of that cooperative, including the amount of energy billed. Energy cooperatives also do not have to obtain property rights arising from certificates of origin in order to redeem them, and fulfill the obligations relating to energy efficiency and capacity fees).

#### *2.3. Legal Conditions and Assumptions for Energy Cooperatives*

The institution of an energy cooperative is subject to a number of requirements and assumptions arising directly from the legislation [16], which must be met in order for an entity to be considered an energy cooperative. According to the authors, in order to better understand the subject of the analysis and the clarity of the arguments presented in the following sections, it is necessary to specify all requirements for the establishment and functioning of energy cooperatives:


#### **3. Materials and Methods (Optimization Model)**

#### *3.1. Assumptions for Creating a Sample of the Energy Cooperative for Simulation Purposes*

In order to carry out the simulation and study the hypotheses, it was necessary to prepare several simulation scenarios that reflect the possible reality of energy cooperatives. Based on actual data on energy producers and consumers in rural areas in Poland, five types of energy cooperative were created for simulation. They were developed so as to reproduce different: (i) locational nature, (ii) level (scale) of electricity demand, (iii) nature of economic activity of the participants in the cooperative, (v) profile of electricity consumption of each member of the cooperative, (vi) generation potential among the members of the cooperative, (vii) level of supply voltage of the members of the cooperative and (viii) size.

The structure of energy cooperatives also takes account of formal and legal aspects resulting from the regulations in force. In particular, the location criterion for the allocation of members in up to three neighboring rural or rural-urban municipalities and low or medium voltage supply was maintained. The criterion for the selection of the generation structure by the optimizer took account at least 70% of the energy demand within the annual billing period and different types of generation.

Due to unfavorable hydrological conditions in Poland, which translate into stagnation in the construction of new hydro power plants [47], it was assumed that a maximum of one hydro power plant [48] may operate within the energy cooperative. The members of the cooperative were selected so that there was a watercourse in their municipalities that could be adapted for the construction of a small hydro power plant. A practical assumption was adopted, stating that a small hydro power plant is characterized by low capacity of between several dozen and several hundred kW. The simulation, therefore, took account of the capacity limits of a single source from 0 to 500 kW with increments of 50 kW. Discreet increments make the simulation realistic because a source with continuous capacity cannot currently be installed.

In Poland there are moderately favorable solar conditions, but the prosumer energy is practically based 100% on photovoltaic sources. The structure of photo-voltaic (PVPP) sources is currently the most popular and fastest-growing method to achieve energy selfsufficiency in Poland [49]. According to the data from the Ministry of Development, Labor and Technology [50], at the end of September 2020, there were about 357,000 micro-systems in Poland (increase of 35.5% compared to the end of the 2nd quarter of 2020 and as much as 131% compared to the end of 2019) [51]. The total PVPP capacity in the Polish power grid is about 3420 MW [52]. The dynamics of micro-systems growth is influenced by numerous aid programs (The Importance of Renewable Energy Sources in Poland's Energy Mix) [53]. The development of photovoltaic sources is also influenced by the economic aspect and the constantly decreasing unit cost of energy generation (LCOE) [54] for this type of source. In view of the above, for the simulations it was assumed that at least 25% of energy production of the members of the cooperative is from solar energy. In addition, capacity limits for a single PVPP farm from 0 to 1000 kW with increments of 50 kW were adopted.

Rural and rural-urban areas are very often undeveloped or have low-rise buildings. These factors support the construction of low-mast wind sources with low and medium capacity. The efficiency of wind generation is about twice as high for Polish wind conditions as for photovoltaic sources, which makes this type of generation attractive in terms of efficiency and cost [55]. For analysis and simulation, the possibility of cooperative participants establishing sources with a capacity from 0 to 1000 kW with increments of 250 kW was assumed.

The development of energy cooperatives must ensure that they are at least 70% selfsufficient in energy per annum and that they have a stable daily and hourly generation profile. The achievement of these indicators is determined not only by the level of installed capacity and the efficiency of generation, but also by its stability and the resultant consumer and generation profile. Taking into account the location criterion when establishing the cooperative was also intended to take advantage of the agricultural character and potential of the regions. In this context, it was assumed that members of the cooperative can also build generation sources with a stable generation profile based on biomass and biogas. For the simulation, the capacity both of biomass and biogas sources was limited to 0 to 600 kW with increments of 200 kW. The presence of generation sources of both stochastic (PVPP, wind) and stable (biomass, biogas) generation in energy cooperatives will result in flattening of the profile and reduction in generation differences between seasons of the year or times of day.

Due to the fact that the installation of new sources involves significant costs, which are not analyzed in the model presented in the paper, the assumption was made that for the optimal balance of demand in the cooperative one member has at most two energy generation sources, which does not exclude a situation where not all members have them and are thus energy producers. The discount nature of the operation of the energy cooperative and its members means that the loss of some energy on its introduction into the distributor's network and its subsequent consumption should be balanced by a slight increase in the installed capacity of the source. For the simulation, it was assumed that the total annual energy production of each member of the cooperative could not exceed 120% of the annual energy demand. This level ensures that each member of the cooperative is fully balanced at an individual level and allows for developing self-sufficiency at an aggregated cooperative level. The discount model is also characterized by the fact that the temporary production surplus fed into the network is continuously accumulated in the network in a follow-up manner, making it possible to use this energy and consume it during periods when the demand is not covered by the current generation.

#### *3.2. Characteristics of Energy Cooperatives Adopted for Simulation Purposes*

Energy cooperatives were established on the basis of current measurement data and profiles of consumers and generation for each type of renewable energy source. The purpose of selecting the participants of the cooperative was to reflect:



**Table 1.** Characteristics of each analytical scenario.


**Table 1.** *Cont.*

<sup>1</sup> Agricultural activity profile: 01.11.Z—Growing of cereals, leguminous crops and oil plants, for seeds, except rice; 01.13.Z—Growing of vegetables and melons, roots and tubers; 01.19.Z—Growing of other non-perennial crops; 01.43.Z—Raising of horses and other equines; 01.46.Z—Raising of pigs; 01.47.Z—Raising of poultry; 01.50.Z—Mixed farming; 01.62.Z—Support activities for farm-animal production. <sup>2</sup> Tariff group: The first character (C, B) refers to the tariff type, C—low voltage, B—medium voltage; The second character (1 or 2) refers to the installed capacity level, 1—up to 40 kW, 2—above 40 kW; The third character (1, 2 or 3) indicates the number of time zones; The fourth character, if any, indicates how to account for the time zones, a—division into peak and off-peak, b—division into day and night.

**Figure 1.** Average daily-hour profiles for each tariff.

The imposition of the above criteria made it possible to map the structures that combine all the above features in order to reproduce the real conditions for the establishment

and operation of cooperatives as faithfully as possible and to examine the hypotheses formulated. Table 1 shows the characteristic and most important features of each of the cooperatives developed for the simulation.

#### *3.3. Optimization Model*

The paper's results were obtained on the basis of data from a simulation of a dedicated mathematical model (the mathematical model has been developed and included as Supplementary Materials (see "mip\_model.pdf" and "mip\_model.tex" files). The mixed integer programming technique [56] was used for modeling. GLPK software was used in particular for modeling the high-level GMPL language [57] (this is open-source software). The COIN-OR/CBC software [58] (also open-source) was used to solve the individual optimization tasks. The model's basic assumptions will be discussed below01 and fragments of the GMPL model will be illustrated.

The input data for the model comprised a two-year horizon data in hourly granulation. The calculation sessions used current data from several dozen consumers from different billing tariffs. The current two-year generation profiles of the following electricity sources were used: a small hydro power plant, a wind power plant, a photovoltaic power plant, wastewater- and biomass-based biogas plants.

Generally, the task of the optimization model was to select the optimum production mix for the pre-set demand, minimizing the energy not taken from the network deposit and purchased from the network. The energy demand, depending on the calculation scenario, was created by individual consumers or aggregated consumers within predefined cooperatives. The energy mix is to be understood as the vector of discrete factors scaling the generation profiles of the energy producers considered. The coordinates of this vector are fixed during the optimization period. The business process modeled connected the consumers with the sources on a proprietary basis (the consumer was the source owner/prosumer). Properly produced energy could be a discrete multiple of the profile adopted. Below is a relevant fragment of a mathematical model in GMPL language.

```
subject to def_ProductionMultiplier{e in EnergySources}:
  ProductionMultiplier[e] = ProductionDiscretization[e]*ProductionDiscretizationLevel[p];
subject to def_Production{e in EnergySources, h in Hours}:
  Production[h,e] = ProductionMultiplier[e]*ProductionProfile[h,e];
```
The ProductionMultiplier[e] is an integer variable defining a multiple of the standardized production of a specific source at hour h, i.e., ProductionProfile[h,e]. Total production of the source e is expressed by the variable Production[h,e].

The values of the total integer vector ProductionMultiplier[e] were concretized as part of the optimization task, for each energy source e. The Production[h,e] vector expressed the energy production by the source e at hour h. The energy produced could be consumed as part of own demand, or could be sent to the network to be recovered from it when needed, with a specific discount. In addition to using energy from self-consumption production and energy previously accumulated in a network deposit, the consumer could buy additional energy in the case of an absence of energy in the network deposit. The energy balance equation in the GMPL modeling language is presented below.

```
subject to def_EnergyBalance{h in Hours}:
  EnergyDemand[h]
  =
  BuyFromNetwork[h]
  +
  sum{e in EnergySources} Production[e,h]
  -
  SendToNetwork[h]
  +
  PickUpFromNetwork[h];
```
where EnergyDemand[h] is energy demand at hour h, BuyFromNetwork[h] is energy purchase at hour h, Production[h] is energy production at hour h, SendToNetwork[h] is energy sending at hour h, and PickUpFromNetwork[h] is energy collected at hour h.

The model assumes that it is not possible to send energy to the operator and take energy from it or collect energy previously sent at the same hour h. The relevant model equations are as follows:


The variables SendToNetworkIndicator[h], BuyFromNetworklndicator[h], PickUpFrom-NetworkIndicatorl[h] are binary variables indexed by the hours of the optimization horizon, which took the value 1 for non-zero values of the corresponding current variables and the value 0 for zero flows.

The equations modeling the operation of a network deposit of energy produced by prosumers and sent to the network for later recovery are as follows:

```
subject to def_EnergyStorage{h in Hours}:
  Storage[h] =
  if(h in StartsOfBillingPeriods) then
    0
  else
  (
    Storage[h-1]
    -
    PickUpFromNetwork[h]
    +
    Discount*SendToNetwork[h]
  );
```
where the set StartsOfBillingPeriods was a set of indexes containing the beginnings of billing periods—especially h = 1, i.e., the beginning of optimization, and Storage[h] is the state of the network energy deposit (storage) at hour h.

The optimization objective function was the sum of two components—energy taken from the network and energy produced but not consumed. Optimization was to minimize the following objective function.


The set EndsOfBillingPeriods covered the last hours of billing periods.

The optimization covered a two-year horizon and the results are from the first year of optimization. This operation was aimed at avoiding the "end of the world problem"—this is manifested by non-intuitive results in the final optimization period (e.g., zero energy deposit (storage) states in final optimization intervals).

A typical optimization task carried out as part of the research covered approximately 50,000 variables, of which approximately 10,000 were total variables. The limitations were 150% of the number of variables, and the optimization task matrix comprised about 300,000 non-zero factors. Due to the volume of output data (many variables indexed by hours within a year), the analysis of the results of a single optimization session is a complex task. Without additional analytical tools, drawing conclusions from the results of thousands of optimization sessions is an impossible task. For this reason, an algorithm of regression trees [59] was used to analyze the profitability of a cooperative depending on the input parameters. Aggregates based on the correlation of weekly profiles of the cooperative members' demand, aggregates based on the correlation of their annual demand profiles, and types and capacity of installed sources were used to describe the cooperative's profit.

#### **4. Results and Discussion**

The following section presents the results of two series of experiments. The first examined the profitability of five specific cooperatives described in detail in Section 3. As part of the second series of experiments, which attempted to generalize the results, a total of 5000 cooperatives made up of a set of randomly selected participants were analyzed.

#### *4.1. Hypothetical Cooperatives (Energy Cooperatives Adopted for Simulation)*

Table 2 presents a summary of the most important information concerning farms that are prosumers or energy consumers and that have become members of the energy cooperatives established for the purpose of the research. The generation structure in prosumer farms was based on various renewable technologies and fuels. The average daily production level in prosumer farms varied between 13 and 100 kWh. It is worth noting that photovoltaic sources dominated the generation structure, whose production characteristics resulted in generation shortages at night and capacity surplus at noon. The variability of the daily generation level of individual members of the cooperative ranged between 0 and 1075 kWh and the average daily energy demand was between 0 and 651 kWh.


**Table 2.** Information concerning farms (prosumers and energy consumers) before the establishment of the cooperative.

PVPP—Photovoltaic power plant; SHPP—Small hydro power plant; WPP—Wind power plant; BMPP—Biomass power plant; BGPP— Biogas power plant.

> For each member of the five cooperatives analyzed, the optimization task was solved and the production mix, total energy consumption and energy loss within the network deposit—unused in the annual billing period—was determined. The simulation results for energy cooperatives and the aggregated results for the individual members are presented in Table 3.


**Table 3.** Simulation results for cooperatives compared to the results obtained by aggregating partial results of the individual farms.

The analysis of the calculation results leads to several key conclusions:


including both the electricity component as a commodity and full distribution fees relating to its delivery. The loss of electricity in the deposit after the billing period is closed is of a similar nature.

• In order to present the economic benefits of the operation of energy cooperatives, Table 5 presents the results of analyses for the example cooperative CP4 (Source data, calculation formulas and results are available as Supplementary Materials—at public source file "Analysis\_CP4.xls") taking account of the costs of energy, costs of distribution and power (capacity market) fee, broken down into: (i) costs incurred individually by farms, (ii) costs incurred by farms as prosumers, (iii) costs incurred by farms—members of cooperatives. The calculations were made based on the actual tariff rates [60]. Due to the complexity of the economic analyses, their complete picture is an area of separate analyses and publications conducted by the authors' team.

**Table 4.** Energy self-consumption at the level of the energy cooperative and the individual members.



**Table 5.** Economic account results for energy cooperative (CP4).

The economic analysis of the selected energy cooperative (presented in Table 5) proves the profitability of its establishment and operation. The analysis does not take account of the fixed distribution and settlement fees, as these are the same at each stage. Additionally, similar to the assumptions of the model, the analysis of investment outlays was not included. Investments in generation sources and the related cost depend not only on the technological solutions used, but also on many ways of financing. It is possible, for example, to obtain subsidies or loans under national and regional support schemes, to participate in RES auctions, to participate in the feed-in tariff or feed-in premium mechanisms. The parameterization of grants and loans is conditioned by many (frequently changing) factors—the above makes it impossible to carry out a synthetic analysis and include it in the article [61].

• Self-consumption can be considered as a parameter for optimal adaptation of the generation profile to the consumption profile. In the analytical scenario before the establishment of energy cooperatives, the average self-consumption in prosumer farms ranged from 37% to 57%. The establishment of the cooperative made it possible to achieve simultaneous generation and consumption at 54–71%.

The solution to the optimal task within the production resources held by its individual members consisted in determining for each cooperative the equivalent of total energy consumption from the network and total uncollected energy within the network deposit, as shown in Table 6.


#### **Table 6.** Evaluation of optimization results.

It is worth noting that in the case of cooperatives CP1, CP2, CP4 and CP5, the profitability was of 7.7% to 27.5%. At −17.93% cooperative CP3 turned out to be unprofitable. This result was different to the authors' expectations and was the starting point for designing and conducting the second series of experiments.

#### *4.2. Random Cooperatives*

As part of this experiment, the profitability of cooperatives selected at random from the previously prepared data of about 100 prosumers was analyzed. As in the first experiment, the individual prosumers' data were prepared on the basis of current data. The composition of the cooperative was drawn from this set and then the optimal tasks for the individual members and the cooperative were solved. Cooperatives with 10, 20, 30, 40, 50 members were considered. The main result of the experiment is shown in Figure 2. A single point

describes the profitability of the cooperative. On the horizontal axis, the total installed capacity of the cooperative and on the vertical axis, the profitability of the cooperative as a percentage value are provided. The experimental points corresponding to cooperatives with a certain number of members are marked in one color. Additionally, regression straight lines were applied to each such group of points.

**Figure 2.** Visualization of the profitability of the energy cooperative by number of members.

The statistical results determining the level of profitability as percentages are presented in Table 7. The first column indicates the number of members of the cooperative. The following lines contain statistics corresponding to a cooperative with a certain number of members. The second column contains information on the minimum value of the profitability of the cooperative. This is followed by the 5%, 25%, 50%, 75%, 95% quantiles and the average profitability. The final columns comprise the maximum value, standard deviation and number of observations greater than zero.

**Table 7.** Statistics of the profitability of the cooperative depending on the number of members.


Table 7 shows that the average profitability of cooperatives with different numbers of members is similar and amounts to around 2%, but the risk of losses varies greatly. As

#### the number of members increases, the respective profitability distributions become more concentrated—see Figure 3.

Experimental data were analyzed using a regression-tree algorithm in order to detect the rules governing cooperatives' profitability. Separate trees were created for cooperatives with the same number of members. As variables describing profitability, the aggregates of correlation matrices of average weekly profiles in hourly granulation and average annual profiles in weekly granulation were assumed; information on the structure and capacity of sources installed were also used. The set of decision-making rules makes it possible to conclude that in order to maximize the profit of the cooperative:


It is worth noting that as the number of members increases the risk of losses (negative profitability) decreases. As the number of members increases, the minimum profitability increases and the standard deviation decreases.

#### **5. Conclusions**

The results of the research and simulations confirm that the profitability of energy cooperatives is highly dependent on the nature and supply and demand profile of its members. The analyses unequivocally confirm that the more numerous an energy cooperative in which the daily-hour profiles of its participants are maximally negatively correlated, the higher is the probability of positive profitability, understood as the minimization of the sum of energy consumption from the network and energy loss in deposit. However, positive profitability in such a scenario means lower profitability than would be the case for cooperatives with fewer members. The level of profitability is, therefore, limited, but the probability of achieving it increases.

The establishment of energy communities on the basis of energy cooperatives has not yet been practically analyzed in Poland. In the authors' opinion, this phenomenon is caused by the necessity to carry out a dedicated and non-trivial optimization analysis each time confirming the profitability of establishing a cooperative, which is proved by this research. However, the analyses suggest that in most cases there is a solution that guarantees the creation of a profitable cooperative. This is confirmed by the results of the simulation, in which the objective for the first group of simulations was achieved in four out of five cases. The second group of calculations each time concerned 1000 simulations, for each of the five variants with 10, 20, 30, 40 and 50 members of the cooperative. The results obtained—respectively 683, 718, 695, 847 and 899—also confirm this thesis.

It is worth noting that the legislative framework determining the functioning of energy cooperatives in Poland imposes relatively unfavorable boundary conditions for their development. In particular, the high discount rate of 1/0.6 is a limitation for the establishment of cooperatives. It limits their profitability in the context of the possibility of independent functioning of farms in the prosumer area and the achievement of discount rates of 1/0.7 or 1/0.8. It is also worth emphasizing that the negative impact of a high discount factor (ratio) can be limited by the physical storage of surplus energy and minimizing its flow through the operator's network. Work is currently underway in Poland to develop and launch support schemes for the construction of energy storage facilities, which will certainly have a positive impact on the increase in the profitability of energy cooperatives. In the authors' opinion, the legal solution in Poland and—as shown in the paper—giving measurable effects in most cases, may also be applicable and scalable in other EU countries obliged to build distributed and civil energy (corresponding to the Community framework imposed by institutions such as REC and CEC) [8,11].

In order to fully assess the profitability of cooperatives, financial aspects not covered by this paper (excepting the analysis for CP4—see Table 5) should also be taken into account, including both the amount of capital expenditure and operating costs. Market conditions, including current electricity prices and transmission and distribution fees, are also becoming crucial in this context—as well as energy storage (batteries) and e-mobility chargers. Due to their complexity, these elements will constitute an area of further exploration, research and publication by the authors' team.

**Supplementary Materials:** The data presented in this study can be found here, https://www.mdpi. com/1996-1073/14/2/319/s1.

**Author Contributions:** Conceptualisation, J.J., M.K., M.S.; methodology, M.K.; formal analysis, J.J., M.K., M.S.; investigation, J.J., M.K., M.S.; resources, M.S.; writing—original draft preparation, J.J., M.K., M.S.; writing—review and editing, J.J.; visualisation, M.K.; supervision, J.J., M.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Complex Valuation of Energy from Agricultural Crops including Local Conditions**

**Václav Voltr 1,\*, Martin Hruška <sup>1</sup> and Luboš Nobilis <sup>2</sup>**


**Abstract:** This paper provides values of economic, energy and environmental assessments of 20 crops and assesses the relationships of soil-climatic conditions in the example of the Czech Republic. The comparison of main soil quality indicators according to the configuration of land and climate regions is performed on the basis of energy and economic efficiency as well as a comparison of the level of environmental impacts. The environmental impacts are identified based on the assessment of emissions from production and also in the form of soil compaction as an indicator of the relationship to soil quality. As concerns soil properties, of major importance is soil skeleton, slope of land and the depth of soil, which cause an increase in emissions from the energy produced. Substantially better emission parameters per 1 MJ through energy crops, the cultivation of perennial crops and silage maize has been supported. Among energy crops, a positive relationship with the quality of soil is seen in alfalfa, with a significant reduction in soil penetrometric resistance; energy crops are also politically justifiable in competition with other crops intended for nutrition of population. The main advantage of energy crops for the low-carbon economy is their CO2 production to MJ, which is almost half, especially in marginal areas with lower soil depths, slopes and stoniness, which can be included in the new agricultural policy.

**Keywords:** energy crops; gross margin; local conditions; climate; soil; modeling; LCA

#### **1. Introduction**

The relationship between food production, energy and the environment is currently an essential issue faced by agriculture [1]. Soil as a means of crop production is subject to many relationships associated with nutrition of population, environmental cleanliness as well as the need to ensure a sustainable source of energy [2]. The problem is escalating due to the necessity to secure food for the growing population [3–5], while responding to changes in farming conditions as a result of climate change [6,7]. A need arises to more accurately specify the production that will be politically justifiable. It turns out that the priority for political decision-making is the food and nutrition security of the population, but a wide-ranging discussion has emphasized the additional potential of energy generation from agricultural products. The entire process has not been adequately specified as yet due to insufficient knowledge of the context of agricultural production with regard to the referred to aspects and diverse conditions [8]. Discussions have been opened up on the use of straw for energy purposes [9], but there are also other matters to be addressed apart from the sufficient volume of production, which also concern the organic matter in soil [10,11].

Agricultural production has diverse impacts on the environment, economy and energy production depending on the relevant conditions [12]. Determination of soil and climatic conditions for crop production and their impact on energy and the environment are crucial for drafting the supporting documents for the purpose of analyzing the relationship between the energy production possibilities and the environment as well as for agricultural policy-making. The existing data provide good quality information regarding the individual crops in the form of a case study helping to identify mutual relationships. However, a

**Citation:** Voltr, V.; Hruška, M.; Nobilis, L. Complex Valuation of Energy from Agricultural Crops including Local Conditions. *Energies* **2021**, *14*, 1415. https://doi.org/ 10.3390/en14051415

Academic Editor: Vitaliy Krupin

Received: 31 December 2020 Accepted: 27 February 2021 Published: 4 March 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

systematic overview of crop production as a whole, including energy production and links to the environment, is missing. The submitted paper uses the comprehensive information on land resources in the Czech Republic and draws up mutual interactions of economic and energy nature that are dependent on the main characteristics of climatic and soil conditions in the Czech Republic. The paper determines the energy and economic margin based on the cost–benefit analysis by crop production technology, and also a comprehensive relationship to the environment based on the life cycle assessment (LCA) analysis for a total of 18 impacts in line with the midpoint analysis. Matters of evaluation of production with respect to decision-making on support, location and potential of agricultural crop production also for energy generation are addressed by numerous recent publications [13–15]. Multiple issues arise that are difficult to resolve by the agricultural policy unless the main economic, energy and environmental context is known well in advance [8].

These matters are extensively covered by the literature. Nonetheless, for the evaluation of all relationships, no comprehensive data are available on the entire territory and production structure. These issues are therefore mostly reflected only with respect to the production of one or more crops based on a model solution or a case study [14].

All the explored parameters, namely the economic, energy as well as environmental parameters, should be subjected to a comprehensive analysis in order to find the optimal future use of crops. Razm [16] in his production assessment used the LCA model in order to achieve the Pareto optimality in assessing the environmental and social impacts of crop production. This procedure can also be applied to seeking the optimal production when making decisions on the use of biofuels.

Some countries have searched for the missing framework for crop production assessment—e.g., development of supporting documents for agricultural crop production in Denmark should be based on a review and an assessment of publicly available databases, inventory reports and scientific literature on measures in the field of governance and their effectiveness with respect to legislation, agreements, conventions and standardizations (Bentsen [17]). The main reason behind this is the necessity to promote the environmental sustainability represented by greenhouse gas emissions from the agricultural sector, soil carbon sequestration, water quality, and biological diversity.

The bioeconomy plays an important role in replacing fossil fuels and is the key factor for sustainability. Wohlfahrt [8] stresses the socio-ecological concept of its exploration, the importance of knowledge of individual territories, flexibility of business activities of subsystems and local regulatory instruments. This justifies the necessity to develop an integrated model approach with various subsystems and heterogeneity. This builds on the assessment of agricultural land composition and its configuration.

#### **2. Materials and Methods**

The energy plan of the Czech Republic provisionally estimates that with a decrease in the production of biofuels from 11,093 TJ in 2020 to 9276 TJ in 2030—i.e., a decrease by approximately 15%—that biogas production should fall from 22,856 to 20,166 TJ—i.e., by 12% [18]. This plan states that the value of agricultural production is very uncertain in the future and depends mainly on the setting of the rules of the Common Agricultural Policy (CAP). Further development of the trend is based on a careful evaluation of all aspects of energy production, including in terms of the function of energy production in the landscape.

Overall energy effectiveness of production is conditional on the choice of crops in the given location, while respecting local conditions which depend on the particular type of farming of agricultural holdings and may vary according to the needs of animal production. Fundamental studies necessary to derive energy indicators have been addressed by a number of authors [19–21], and data for the Czech Republic from the IAEI survey and Preininger [22] were used in this analysis.

In the Czech Republic, a permanent monitoring system of agricultural production was developed based on the evaluation of evaluated soil ecological units (ESEUs, in the Czech Republic called BPEJ, introduced in the Czech Republic in 1970). The system is based on the classification of climatic factors within the climatic region (Appendix C, Table A2, the main soil unit (MSU (HPJ)), describing the main pedological characteristics of soil, and on the description of terrain configuration: slope (◦), deep of the soil (cm), stoniness (%), and by the area in the Czech Republic [23]. MSUs are laid down in a decree [24], but more information is provided by the tracking of the Research Institute for Soil and Water Conservation [25]. In the Czech Republic, a total of 2199 ESEUs have been classified that are mutually compatible throughout the territory of the Czech Republic. Based on the definition of these units, a paper was elaborated in order to cast more light on the links between the production and soil-climatic conditions—e.g., the modification of economic indicators based on production functions [26]. Even though the evidence of ESEU is domestic, the obtained generalized information can also be used for assessments in other countries.

Environmental indicators are essential to assess any production. The LCA assessment of crop growing impacts is described, e.g., by [27–30], and preparations are carried out for individual evaluation of technologies for the size of emissions, especially CO2 [31].

This study is based on the values included in the Agri-footprint 4.0 database [32] and impact categories of the ReCiPe method were used. Model processes, based on Agrifootprint database processes, were modified on the basis of specific data for the Czech Republic. The adequacy of the modified processes was verified by comparison with the results of the original Agri-footprint processes and the Ecoinvent 3 database.

The data included in the national database of soil economic information were used to set the main yields and inputs and are subsequently updated in line with the soil and climatic conditions in the Czech Republic. The statistical survey is based on a sample cost survey of approximately 250 agricultural enterprises and the results are processed according to the IAEA methodology [33,34].

Information on crop yields and costs on individual soil-climatic conditions was used to calculate total emissions in individual categories according to the Agri-footprint database [32] and to calculate crop production in MJ. The resulting ratio was assessed against the description of soils in the Czech Republic [35].

The current papers add more information on ESEU and thus offer a comprehensive picture of mutual inter-relationship of economic, energy and environmental aspects [36–38]. Figure 1 shows a comprehensive monitoring system based on available data, which are validated and specified against individual ESEUs. The diagram shows the sources for processing the economic and energy data of crop yields, technologies and their costs as well as the composition of crops, including links to the calculation of emissions from Agri-footprint data.

The main scheme of calculation is given in Appendix A. It is used to calculate the economic, energy and environmental data.

The basic approach consists in the cost–benefit analysis of production of individual crops.

**Figure 1.** Processing of economic, energy and environmental data for calculation.

#### *2.1. Method to Determine the Economic and Energy Values*

When assessing the production, the indicators of economic and energy gain (Gross margin including overheads) were determined based on the production value once the necessary operating and overhead costs were deducted [39].

$$GMo\_{i,p} = SO\_{i,p} - COGso\_{i,p} \tag{1}$$

where: *GMo,i,p* is a Gross Margin with overheads for crops *p* and soil-climatic conditions *<sup>i</sup>*, *COGSo* = cost of goods sold including overheads (EUR·ha−1) and *SO* is a standardized output of crop products (EUR·ha<sup>−</sup>1). More details are provided in Appendix B.

This procedure was opted for due to the need to calculate the total costs of production for the purpose of assessing the economics of farms according to individual crops by the Institute of Agricultural Economics and Information (IAEI) and it is reflected in all the supporting documents [40].

The overheads are derived from the economic data on agricultural holdings ascertained by the IAEI survey and, with respect to energy, the same value was used as the costs of working operations in in the overhead costs to direct variable crop production cost ratios. The reason thereof is primarily the burdening of production of some crops (e.g., potatoes) with high overhead costs of postharvest treatment and storage.

The market price of agricultural production resulting from the IAEI statistical survey and the resulting price depends on the yields corresponding to the given soil and climatic units according to ESEU.

Energy gross margin including overheads 2 is similar:

$$EGMo\_{i,p} = ENS\_{i,p} - ECOGSo\_{i,p} \tag{2}$$

where: *EGMo,i,p* is the energy gross margin with overheads for crops *p* and soil-climatic conditions *i*, *ECOGSo* = energy of cost of goods sold (GS) including overheads (Tables A4–A7 MJ) and *ENSi,p* is the standardized output in MJ (Table A3).

The energy values of *EGMo, i, p* production were evaluated on the same inputs and outputs as *GMo* (1). The primary energy values of the costs are derived from weight of the machines in kilograms listed in the database according to the example in Table A1, where the weights (in kilograms) of machines needed for the production inputs are described. The value of primary energy per kilogram of weight (Table A5) is divided by the number of years of depreciation and by the number of hectares processed per year. Energy of fuel (Table A6) is given by fuel consumption for work operations and for maintenance on the basis of fuel consumption equivalent, fuel consumption for the transport of materials and technological equipment, according to the energy of organic and inorganic fertilizers (Tables A6 and A7) [34], and protective equipment [41]. The costs were calculated by the Institute of Agricultural Economics and Information (IAEI) [36]. The costs of transport of material were calculated for the standard distance of 5 km between the farm and the land. The calculation of costs also included the labor costs based on the average labor rates in agriculture in the last 5 years.

Soil conditions were determined by soil classification in the ESEU system. Data on slope, soil depth and percentage of stones over 2 mm in the soil were evaluated in the physical units.

The economic values of yields and inputs into the soil for soil-climatic conditions in the Czech Republic were compiled according to the database of ESEUs rated [33,35,36]. Earlier data on revenues for ESEU based on the data of 1970 have been updated by a detailed survey of 529 plots conducted over a period of 9 years (2002–2010). The yield (*Y*) design was based on the production functions of the dependence of yields on natural and technological conditions [38] according to Equation (3).

$$Y = f(\text{Wt}; \text{S}; \text{Z}; \text{ P}; \text{ P}; \text{ L}; T) \tag{3}$$

where *Y*: yield of crops, *W*: variables of temperature, precipitations and soil moisture, *S*: soil type, sort and conditions, *Z*: nutrition of nitrogen, phosphorus and kalium; *P*: number of chemical protection operations, *L*: cultivation of soil and *T*: progress of technology.

The underlying values for yields and similarly for nitrogen dosing and the chemical treatment application were compiled according to the statistical valuation of the given environmental conditions [39].

According to the identified functions, the yields were standardized to the remaining soil-climatic conditions. Subsequently, the proposed value of standard yields was validated with the current value of yields under the given conditions, and a new standardized value of yields was proposed for ESEU. A similar function such as the derivation of the yield (Relationship 3) was compiled by the dependence of nitrogen doses on soil-climatic influences.

An example of a comparison of actual and standardized results of production functions for yield of winter wheat is shown in Figure 2.

The compiled standardized yield values correspond to the categories of soil-climatic conditions for which the values are determined. The climatic factors that are most important for the achieved yields are therefore always calculated for the relevant classification scale and its values—i.e., for climatic regions 0–9 (Table A2). When evaluating specific yield conditions, there is always a deviation from the standardized values due to the achieved weather values, which similarly applies to the monitored soil values. Nevertheless, the database is based on the balanced properties given by long-term observation and statistical analysis of individual effects. For the purpose of this article, the data are sufficiently informative even if they do not meet the requirements of directly measured values, and thus the statistical results are affected by a certain similarity of climatic and soil influences within specific groups of conditions.

**Figure 2.** Comparison of winter wheat yields and yield prediction using the production function [38].

For the economic evaluation of the cultivated crop, the economic efficiency *ηEp* was determined according to the Equation (4).

$$
\eta E p\_{i,p} = SO\_{i,p} / \text{COGS} o\_{i,p} \tag{4}
$$

as the ratio of the value of output (*SO*) to the value of input (*COGS*). Energy efficiency *ηEnp* is computed similarly in Equation (5).

$$
\eta Exp\_{i,p} = ENS\_{i,p} / ECOGS\_{i,p}.\tag{5}
$$

#### *2.2. Assessing the Environmental Impacts of Crop Production*

The assessment comprises the methods determining the formation of emissions from crop production on soil, water and air as well as physical impacts of production on the quality of soil in the form of soil compaction.

Determining the Formation of Emissions on the Environment

The content of the evaluation in this article is mainly the ratio of individual types of emissions and the achieved energy of outputs, which evaluates the relationship of individual ESEUs and crops to emissions. The main indicator assessing the environmental impact of production used in this paper was the ratio of total emissions of individual types of indicators to the total crop production including the by-product [42].

Environmental impacts were added on the basis of a description of specific emissions (midpoint) and the system allows global life cycle impact (endpoint) for selected crops [39]. Values are based on the results of the ReCiPe method assessment of primary data for the Czech Republic and the secondary results are based on the Agri-footprint LCI database.

In this context, the stages of the product life cycle are divided into: upstream—processes preceding the actual manufacturing of the product, core—actual manufacturing of the product and downstream—processes following the manufacturing of the product.

#### *2.3. Model Processes to Determine Emissions*

The model processes are based on the Agri-footprint database; they were modified using the specific data of IAEI and are based on the data for energy evaluation—the weight of machinery, repairs (equivalent in l/ha of consumed diesel), transport costs (energy requirements of transport in MJ/ha), consumption of fuels and chemical protection (necessary technology in MJ/ha and weighted dose of pesticides in kg/ha) were expressed as diesel consumed by diesel engine of an agricultural machine (energy, from diesel burned in machinery/RER economic) [41,43–45]. The impact of fertilizers was calculated for crop inputs of N, P2O5, K2O, MgO, CaO, and S, and the emissions factors were derived from Agri-footprint database. The emission size relationship is based on the source data of the Agri-footprint database [32,46]. Organic fertilizers were calculated as manure in accordance with the database documents at the level of ESEU. Emissions to air mainly include nitrous oxide, ammonia and pesticide residues, carbon dioxide emissions, which as a reaction of soil with limestone and urea were not included due to the lack of specific data on consumption. Based on the specific data, emissions from minerals, livestock manure and pesticides were recalculated and adjusted. Emissions from crop residues remained the same as in the original process. Emissions to water from mineral fertilizers and livestock manure and pesticides were recalculated and adjusted on the basis of specific data, and emissions from crop residues and heavy metals were assessed according to the original process. Emissions to from soils were based on the specific data; emissions from pesticide residues were recalculated and adjusted and heavy metal emissions were used from the original process.

The ratio of total emissions of individual types of indicators and the energy contained in the total production of the crop, including the by-product, was used as the main indicator for assessing the ecological impact of production. An overview of the average energy efficiency of crops in the Czech Republic is given in Figure 1. To assess the impact of emission to MJ (*EmMJ*) of produced energy, the specific value of emissions per unit of output energy in MJ 6 was used:

$$EmMf\_{i,p} = EMmidp\_{i,p}/ENS\_{i,p} \tag{6}$$

where *EMmidp* is the emission of midpoint classifications as a sum of all included partial emissions of operations, fertilization, chemical inputs and transport, for crops *p* and soilclimatic conditions *i*.

Evaluation of the significance and influence of individual ecological indicators is a separate issue beyond the scope of this work. For their complex evaluation, it is possible to use more methods based on the evaluation of the meaning and weights of individual indicators. Due to the large number of indicators and their various possible interpretations and due to the simplification of the issue, the methodology of multicriteria decision-making was chosen to determine the total emission value per MJ *EEm* according to Equation (7)

$$EEm\_i = \sum\_{E=1}^{k} \left(\sum\_{i=1}^{l} Em\_o/l\right)/k \tag{7}$$

where *Emo* is an order of the value of emission for *ith* crop on ESEU, *E* is a sort of emission, *k* is a number of calculated emissions and *l* is a number of ESEU.

#### **3. Method of Processing**

Databases are maintained in MS Access and MS SQL databases. For each ESEU, selected technology and crop, standardized values of economic and energy efficiency *ηEp ηEnp* as well as *Emo* and *EmMJ* for each environmental indicator were processed.

The supporting documents were elaborated in line with the technological procedures and verified yields of individual crops under the given ESEU. In the system, it is possible to compile procedures for different antierosion methods of soil treatment and for different nitrogen inputs. To compare soil-climatic conditions, the plowing method of tillage was chosen. All individual ESEU categories were always evaluated during the processing. To evaluate the average conditions of crops, weighted averages of indicators of crops were calculated according to the area of ESEU representation in the Czech Republic.

#### **4. Method of Assessment of Crop Impacts on Soil Environment**

The impact of crops on soil compaction was evaluated from a survey conducted in the years 2002–2011; the assessment of the impacts on soil was based on the penetrometric resistance of soil, which is an appropriate indicator of the overall conditions of agricultural land [47,48], bearing in mind the need to obtain information on deeper layers of soil on large areas. Soil penetrometric resistance is closely related to soil-organic carbon (SOC) formation, where soil resistance decreases with higher soil content [49].

The underlying principles of penetrometer measurements are described in the paper by Lhotský [50]. The methodology has been modified to have one sample point for approximately 5 ha. There were three sample points on a plot with the area of up to 10 ha, with another sample point that always added an additional area of up to 5 ha; the sufficient number of sample points, however, was 10. The location of these points was chosen so that they were equally distributed across the entire land plot and were not located in the headland. During each measurement, the probe was pushed into the soil at a constant speed and the penetrometer was reset in cases where the probe hit a stone. Soil samples were collected in each plot in order to determine the soil moisture—namely, from no deeper than the soil tillage depth and from the subsoil layer.

The obtained values of penetrometric resistance are expressed in the form of the mean resistance of three layers—namely, 0–18 cm, 19–38 cm and 39–72 cm.

The results were assessed based on the correlation analysis and show, as well as the direct impact of the crop on the resistance, the general relationships, which determine the effects of penetrometric reistance in the respective soil layers.

#### **5. Results**

The results of the comprehensive assessment of economic, energy and environmental impacts of crop growing are based on the determination of individual soil and climatic parameters of the locations where the crops are cultivated. Altogether, the assessment covered a selection of 20 crops and different options for their use. The assessment of economic indicators builds on the calibrated economic results of agricultural holdings—namely, on the average of the last 5 years. Therefore, the results are stable and independent of the respective year. The economic and energy indicators are based on the cost–benefit analysis, which facilitates the evaluation of the absolute profit per hectare of the agricultural land in monetary or energy terms. These indicators are shown in the figures and tables as the attained efficiencies according to Relationships (4) and (5). The environmental indicators are related to the produced energy in production including straw.

#### *5.1. Relation of Economic, Energy and Environmental Characteristics to Soil-Climatic Conditions in the Czech Republic*

To determine more detailed effects of weather and soil conditions in the Czech Republic on the achieved economic, energy and environmental parameters, the available database data of individual crops and environmental indicators in the database were processed.

The overview of average energy and economic efficiency of crops in the Czech Republic in comparison to CO2 emissions is provided in Figure 3 and Table A8 and the average terrestrial ecotoxicity values are described in Table A9, with individual data provided in a separated file for all emissions [35].

**Figure 3.** Average values of *ηEnp*, *ηEp* and CO2 emission per GJ of energy in the product.

The system enables a comparison of results of *ηEnp, ηEp* and *EmMJ* in the same soil-climatic conditions as well as all the other monitored inputs and outputs. The results of individual crops show the lowest emission load for CO2 per MJ produced for forage bulk crops, the largest load is achieved for crops with food use, where economic efficiency also prevails over energy efficiency. The achieved environmental results depend very much on the technologies used for growing crops and harvesting. For example, alfalfa has almost the same cultivation technology as clover, but its environmental impact reflects a high consumption of diesel fuel, when the silage mass is harvested by high-performance and high-consumption cutters instead of using solar energy for drying. The different value of the energy balance between energy and food crops also provides a new perspective on emissions from animal production, which consumes bulk feeds with better energy efficiency than food production.

In the following section, the main soil-climatic indicators according to the ESEU system were used individually.

The obtained values in line with the ESEU code are included in Figure 4.

**Figure 4.** Main configuration and climatic properties and EGMo, GMo and EEm in average of crops.

From the above dependencies, the importance of land and climate configuration indicators is obvious. Due to the slope of soil, the energy efficiency decreases most significantly, namely, by 1.58, while due to the stoniness of soil there is a decrease of 1.54, and due to the depth of soil, of 1.29. Due to the difference in climate regions of 0.94, the difference in economic efficiency decreases in similar relations, and in absolute values less significantly (values in the graphs are multiplied by 10), but the percentage of the decrease is more pronounced. The percentage changes of all indicators are given in Tables A10–A13.

The results for the main types of soil are shown in Figure 5.

**Figure 5.** *EEm*, *ηEnp*, and *ηEp* among the main types of soil in average of crops.

The results show the main difference value of *ηEnp* is 1.1; there is also an interesting difference between the energy and economic efficiency in chernozem, which is mainly caused by growing economically favorable crops on fertile areas. Higher emissions per energy of outputs correspond to the lower economic and energy efficiency.

#### *5.2. Assessment of Impacts of Chosen Crops on Penetrometric Resistance*

In order to assess the relationship to soil compaction, a survey was carried out measuring the penetrometer resistance by frequency of crops grown on the plots. The assessment also included cases when more than three values of penetrometer resistance for the respective crop were obtained. The correlation analysis (Table A13) indicates the main dependence of the value of resistance in the monitored crop, determining the effects of penetrometer resistance in the respective soil layers.

The results of the survey of penetrometric resistances from the years 2002–2011 [38] are shown in Figure 6.


**Figure 6.** The penetrometric resistance according to the number of crop repetitions depending on the depth of the soil.

The results of penetrometer measurements and the identified trends in soil compaction are included in Table A14, with plotted significant dependences of the penetrometric resistance on the crop, the positive effects of alfalfa in subsoil, and the negative impact of winter rapeseed growing across the soil profile as well as of maize in the bottom layer at the depth of more than 39 cm. The values in subsoil are crucial for the assessment of effects of penetrometer resistance. The values of penetrometric resistance at the depth of more than 39 cm can be influenced by penetrometer measurements that ended prematurely due to the solid bedrock. The largest effect on subsoil compaction is seen in poppy seed (difference of 1.88 MPa) and alfalfa (1.1 MPa). The highest compaction, on the contrary, is reported for winter rapeseed (3.06 MPa) and triticale (1.4 MPa).

#### **6. Discussion**

The main contribution of this article is a comprehensive view of the economic, energy and emission context of the production of individual crops according to soil and climatic conditions. The evaluation of individual factors is based on the standardized values of inputs and outputs of individual crop processing technologies up to the level of work operations. The work thus enables a systematic view of the production structure of farms in their soil-climatic conditions, and thus enables better planning and management of land use in local conditions. The existing information in the literature is fragmented into partial cases under specific conditions, which are difficult to combine into one framework to find complex contexts. The literature presents analyses of individual energy and economic balance of crops, especially according to higher territorial units and countries, or on the basis of a partial calculation of technology data and simulation of operating conditions [51,52]. This issue is addressed on the basis of data of technological processes individually also according to the yield of straw [9,53] or biomass of selected crops [54]; however, the overall crop balance depending on local conditions for energy, economic and environmental concepts is not addressed. Specific conditions by territorial units are determined on the basis of statistical surveys without functional interdependence [9]. This work does not address the individual technological context of the use of new technological procedures, but the basic standardized framework, by which the newly obtained data can be evaluated. The way in which emission data are processed by ordering ESEUs within individual crops allows the impact of emissions on specific businesses and for specific input choices to be adapted. The way in which emission data are processed by ordering ESEUs within individual crops allows the impact of emissions on specific businesses and for specific input choices to be adapted. Environmental impacts are based on the results of ReCiPe method assessment of specific data for Czech Republic combined with model processes of the LCI database Agri-footprint. A system approach to derive emissions based on this database makes it possible to evaluate individual soil-climatic conditions based on the full impact of technologies. The basis is a complete evaluation of emissions according to primary energy in the manufacture of machinery, according to fuel consumption, fertilizers and protective equipment depending on the doses of material and the performance of kits in individual operations in specific soil and climatic conditions. Emission sources are therefore assessed comprehensively and compared to some other sources, which only evaluate some emission components [55]. The standard LCA database evaluation approach allows for crop-specific evaluation but without the choice of individual emission items according to machine aggregations [4,56]. This division makes it possible to adapt the emission factors for the individual difficulty conditions.

A comprehensive evaluation of individual crops shows significant differences between energy and food crops. Higher economic efficiency of food crops is accompanied by increased costs per unit of energy and higher emissions (e.g., soybeans, poppy, sunflower). Higher energy efficiency of feed crops and lower emissions of energy produced can contribute to a further discussion on the focus of food in relation to animal production as well as to discussions on energy production. There are conflicting views on this topic and a detailed LCA analysis of the whole process is needed [57–59]. An important context of the relationship between emissions *EmMJ* from the production of feed crops and grains for human consumption is given in Table 1.


**Table 1.** Emissions per GJ of produced energy between crops for food and energy production.

The table shows that emissions produced from energy crops (fodder: clover grass, clover hey, maize silage) are 42–50% lower per GJ of energy produced than those from food crops (winter wheat, spring barley, peas).

The identified connections between energy, economic and environmental impacts of agricultural crop production show a very significant dependence on soil-climatic conditions. The article separately evaluated the individual properties of land on the operational indicators of crops. The soil depth affects the energy efficiency of crops in the Czech Republic by 15%, the economic efficiency by 21% and the overall order of emissions by 33%. The land slope affects the energy efficiency of crops in the Czech Republic by 18%, the economic efficiency by 23% and the overall order of emissions by 31%. The stoniness affects the energy efficiency of crops in the Czech Republic by 14%, the economic efficiency by 18% and the overall order of emissions by 31%. The results depend on long-term observations of the IAEA and the identification of crop production functions.

Climatic indicators are a factor acting together with soil indicators and according to their specific compositions, overall results can be derived. The interaction is mainly due to the achieved crop yields in specific conditions. From the point of view of the suitability of crops for production, the dependences found show that marginal soils with a shallow soil depth, high stoniness and slope, even on less fertile soils, have higher relative emissions from crop production to 1 MJ. Consequently, there is a need to grow crops in these conditions without large emission effects, especially perennial energy crops, which can be used for both animal production and energy production.

The local conditions also cover the effects on the environment in soil based on the mechanical effects of crop growing on soil. The obtained results suggest major impacts of individual crops on soil conditions. The penetrometric resistance of the soil depends mainly on the content of organic matter in the soil and on the method of farming. The content of organic matter in the soil is ensured both by organic fertilization and in deeper layers, above all by the decomposition of the root system of crops. According to the performed penetrometric survey, less compaction of subsoil and subsoil is found in alfalfa and some springs, spring barley and poppy. In terms of lasting effect on improving the condition of the soil in the deeper layers of the soil, alfalfa is very important crop [60–62]. Global biogas (methane) production needs new opportunities for production using legumes on arable land, as they do not significantly degrade soil quality compared to other crops [63], unlike the cultivation of sown maize [64]. Under the new climatic conditions, there is a significant relationship to precipitation, where alfalfa is highly profitable in dry conditions, while clover in humid conditions [65]. A very important advantage is the high production of roots in depth with a positive effect on the soil structure, the content of soil organic matter (SOM) and consequently also on the productivity of the stand [64]. This makes it possible to improve the sustainability and resilience of the natural environment, in particular with regard to reduced external inputs, improved humus balance (carbon, energy and nutrient

cycle), reduced greenhouse gas emissions and the general positive impact of fodder and catch crops in crop production practices [63].

For the purpose of aligning the growing of crops for food and energy purposes, according to the effects of selected crops on soil ascertained based on the obtained values of penetrometric resistance of individual crops, alfalfa is a highly suitable crop since it improves subsoil compaction and at the same time provides good energy gain. The area under alfalfa, however, substantially decreased in recent years due to the reduction in cattle breeding and has reached its minimum in the Czech Republic. The current need to improve the subsoil conditions together with the need to increase the energy crop capacities, with the concurrent pressure to reduce the cultivation of maize for silage, speaks in favor of its production. Alfalfa can easily be used in all the existing biogas plants, up to a share of 20%, for pellet production and cattle fattening.

The system can analyze 22 environmental indicators in the endpoint category and 18 environmental indicators in the midpoint category [35] and can be combined with the physical effects of crop growing on soil. The physical effects of crop growing on soil constitute an equal impact on the environment as the emissions and assume the form of numerous impacts, especially on soil erosion, soil fertility, resilience to drought as well as water contamination in the case of topsoil wash off. The subsoil compaction keeps increasing as a result of a change in crop composition and climate change, with a decrease in the number of frost days causing soil swelling (frost heaving), as well as an increase in crop yields that have to be harvested and transported from the land by heavy machinery.

#### **7. Conclusions**

The paper describes the process of developing the system of assessment of soil and climatic impacts on individual crops with respect to economic, energy and environmental indicators for the classified unit of soil and climatic properties—i.e., ESEU in the Czech Republic. The main indicator that was selected to compare the individual conditions was the ratio of the value of individual types of emissions per energy output in MJ. Apart from this indicator, other usual indicators were also set such as the energy of production and economic efficiency of production. The statistical results can also be defined for all the other indicators. Aside from direct classification of soil and climatic conditions, other soil properties, available from the monitoring of the Research Institute for Soil and Water Conservation, were subjected to regression analysis.

With respect to emission impacts, perennial energy crops (silage sorghum, sorrel, hemp), should be encouraged. The current status among major energy crops is of the corn silage with good emission characteristics, but it is necessary to ensure the proper growing conditions with regard to soil quality. The exploration of energy outputs diminishes the nutritional properties of food crops. In spite of this, the analysis shows that in terms of emissions the energy crops bring more benefits when grown under marginal conditions, if the cultivation of these crops under the respective conditions is possible. From the point of view of impact on the soil and sustainable development, justified cultivation of alfalfa with a proven influence on the amelioration of compacted soils is crucial. Alfalfa has increased emission effects compared to clover due to harvesting with a high-power cutter. In the case of alfalfa harvesting on hay, its emissions are comparable to clover. Due to the increasingly difficult search for suitable biomass for energy production while respecting the requirements for food production, alfalfa production is a suitable solution for ensuring the quality of soil and replacement biomass for current energy crops. From the point of view of sustainable development, this solution is very essential for obtaining biomass from agricultural sources. The overall use of results should be based on the evaluation of Pareto optimality [16] in line with the current production options and requirements determined by policies and thorough knowledge of territorial aspects of production. For the sake of further development, the use of maps with the impacts of production on emissions under specific conditions is expected. In the future, it is possible to consider a comprehensive assessment of emission effects in agriculture [66].

The article provides a comprehensive view of the joint impact of natural factors on energy, economic and environmental indicators, and thus provides a better picture of their impact on measures for further development of energy in regions and for agricultural policy. As Wohlfart [8] writes, a comprehensive assessment of all contexts is always important for further assessment of a bioeconomy, and therefore also for energy policy. For further development, it is important to compare modeled and measured results in connection with local land conditions for a real evaluation of the conditions of the whole region.

One of the best examples of aligned energy generation and food production is the use of alfalfa as a sanitary crop to address subsoil compaction and as a crop that can help reduce maize silage on the soils at risk of erosion and emissions impact [11,67]. Deeprooting crops are a desirable source of carbon in the deeper layers of the soil, where they also ensure the stability of soil aggregates and sufficient soil permeability. Knowledge of local conditions and their appropriate agricultural use should also become part of the Green for Europe strategy [68], which assumes keeping global warming below 1.5 ◦C while still reducing greenhouse gas emissions. The main advantage of energy crops for the low-carbon economy is their potentially lower CO2 production, especially in marginal areas with less soil depth, slope and stoniness. Higher variability of biomass production in the field, taking into account the requirements of sustainable energy, can also lead to higher deregulation and liberalization of the energy market. See [69] for case of ensuring sufficient biomass capacities.

**Author Contributions:** Conceptualization, V.V. and L.N.; methodology, M.H. and V.V.; software, validation, formal analysis, investigation, data curation, project administration, funding acquisition V.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Národní Agentura pro Zemˇedˇelský Výzkum, grant no QK1710307.Institutional Review Board: Not applicable.

**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The paper was elaborated in the framework of the project of MoA QK1710307 "Economic support for strategic and decision-making processes at national and regional level leading to the optimal use of renewable energy sources, especially biomass, while respecting food selfsufficiency and soil protection". The authors acknowledge the support of the project. The authors also thank Maria Macháckov ˇ á from the IAEI for preparing the materials for the search, Václav Hofman for help in preparing the data and also all other persons who participated in the validation of the input data used for the preparation of materials for the article including Ivo Pokorný for the MS SQL services.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Figure A1.** Scheme of the Comprehensive Information System.

#### **Appendix B. Calculation of Costs and Outputs**

#### *Appendix B.1. Costs*

The evaluation of costs of parameterized production is based on the sum of costs that have to be spent to achieve the production of crop under ESEU, for plowing technology with operations factored in. The variable costs, VCosts 8, were calculated based on the standardized technological procedures for all main crops according to the Institute of Agricultural Economics and Information.

$$V\text{Costs}\_{i,p,r} = \left(\text{WO}\_{i,p,r} + T\text{C}\_{i,p,r} + CM\_{i,p,r} + LC\_{i,p,r}\right) \times \text{CVC}\_{i,p} \tag{A1}$$

where: *i* evaluated soil-climatic unit ESEU; *p*—evaluated crop; *r*—number of operations; *WOi,p,r* = unit costs of work operation in line with technologies proposed by Research Institute of Agricultural Engineering, p.r.i., (EUR/ha); *TCi,p,r* = transport costs (EUR/ha); *CMi,p,r* = costs of material, fertilizers, plant protection products and auxiliary products (EUR/ha); *LCi,p,r* = unit labor costs of per cultivation technology and crop under the given soil and climatic conditions based on the five-year average costs (EUR/hour); *CVCi,p,r* = coefficient of variable costs derived from the IAEI cost survey for ESEU, crop and operation.

The indirect costs of producing of crops are determined with the coefficient *ICfc* 9, which is determined according to the IAEI cost survey as a share of indirect *ICi,p,r* and direct variable crop production costs.

$$I\mathbb{C}f\mathfrak{c} = I\mathbb{C}\_{i,p,r}/V\mathbb{C}\mathfrak{c}ots\_{i,p,r} \tag{A2}$$

#### *Appendix B.2. Outputs*

The price of the parameterized production 10 was determined for standardized yields on ESEU and is composed of the production of the main product and by-product:

$$PO\_{i,p} = Y\_{i,p} \times P\_pCR + Yb\_{i,p} \times Pb\_p \tag{A3}$$

where: *Yi,p* = yield of parameterized production of the main product for the *p*-th crop, which is the corrected normative natural yield of individual main agricultural crops (*p*) for individual ESEU (*i*) [33] (t/ha); the yield is updated annually according to the five-year average of crop yields in the IAEA cost survey and the FADN survey; *PpCR* = normative prices of the main product of individual p-th crops differentiated according to climatic regionalization (EUR/t); it is updated annually according to the five-year average of agricultural crop prices from the IAEI and the Czech statistical office (CZSO) survey; *Yb* = Yield of by-product (straw) on ESEU and crop; *Pb* = a normative price of by-product of crop.


#### **Appendix C**

Source: Research Institute of Agricultural Engineering, p.r.i., 2018.

**Table A2.** Description of the climatic regions.


Source: IAEI new calculation of temperature and rainfall [24].


#### **Table A3.** Energy of crop production.

Source: Preininger [22].

#### **Table A4.** Used unit costs of materials.


Source: IAEI.


**Table A5.** Conversion factors for calculating the energy contained in machines.

Source: Preininger [22].

**Table A6.** Energy of used materials.


Source: Preininger [22].

**Table A7.** Price and energy of nutrients in cow manure.


Source: <sup>1</sup> IAEI; 2,3 Preininger [22].


**Table A8.** Average emissions global warming and efficiency of crops.

**Table A9.** Average emissions of terrestrial ecotoxicity and efficiency of crops.



**Table A10.** Average emissions and efficiency of crops according to the climatic regions.

**Table A11.** Average emissions and efficiency of crops according to the slope.


**Table A12.** Average emissions and efficiency of crops according to the depth of the soil.



**Table A13.** Average emissions and efficiency of crops according to the stoniness.

**Table A14.** Correlation analysis of penetrometric resistance according to the frequency of crops on the plot.


\*\* Correlation is significant at the 0.01 level (2-tailed). \* Correlation is significant at the 0.05 level (2-tailed).

#### **References**


## *Article* **Renewable Energy Generation Gaps in Poland: The Role of Regional Innovation Systems and Knowledge Transfer**

**Patrycjusz Zar ˛ebski 1, Vitaliy Krupin <sup>2</sup> and Dominika Zw ˛egli ´nska-Gałecka 2,\***


**Abstract:** Aim of the research is to analyze regional gaps in terms of renewable energy generation across Poland. For this purpose, four types of regions were outlined based on two indicators: the existing renewable energy generation capacity and the current regional energy demand revealed through the number of residents. This classification allowed to reveal regions in Poland that have distinct features of energy gaps and peripherality, while also more successful regions with renewable energy surpluses and distinct sustainable energy potential. For each of the region type key potential systemic problems in terms of renewable energy generation development were given. To understand how peripheral regions and regions with energy gaps could be supported in their development of renewable energy generation the regional innovation systems, social networks, knowledge and technology transfer and diffusion were substantiated. Results of the research can serve as an aid in development of national and regional energy policies, helping to understand peculiarities of local renewable energy generation and the influence of enabling environment peculiar to the specific region, including the regional innovation systems and intensity of knowledge transfer and diffusion.

**Keywords:** renewable energy; energy policy; regional innovation system; social network; knowledge transfer

#### **1. Introduction**

Renewable sources of energy are among key sustainable development elements that are perceived as the future of our planet and can help in its environmental protection while satisfying the energy needs of global society. Due to the progressive economic development and the growth of the world's population the supplies of various fossil fuels necessary for energy generation are rapidly declining. Yet what is as important—the impact of energy generation from fossil fuels is polluting the environment and causing an intensifying climate change, thus leading to unpredictable consequences and shifts in various areas: weather events, climate zoning, plant growth patterns and biodiversity [1–3], and last but not least—human health [4]. These issues and the fact that their cause is mainly anthropogenic [5–7] is the reason numerous state governments and international organizations are taking actions to intensify shifting the societies and their economies toward sustainable development patterns and thus reducing or even stopping the unfavorable environmental and economic trends.

The European Union is especially active in this sense implementing policies aiming at large-scale transformations toward renewable energy generation, reduction of greenhouse gas emissions, and limiting the pollution of the environment. EU's Climate and Energy Framework [8] is one of such policies setting 2030 as their target year. Its basis is the recast Renewable Energy Directive (RED II) [9] with its key joint EU goals being: (a) at least 32% share for renewable energy, (b) at least 32.5% improvement in energy efficiency, and (c) 40% cuts in greenhouse gas emissions (compared to 1990 levels).

**Citation:** Zar˛ebski, P.; Krupin, V.; Zw ˛egli ´nska-Gałecka, D. Renewable Energy Generation Gaps in Poland: The Role of Regional Innovation Systems and Knowledge Transfer. *Energies* **2021**, *14*, 2935. https:// doi.org/10.3390/en14102935

Academic Editor: Gianfranco Chicco

Received: 30 April 2021 Accepted: 16 May 2021 Published: 19 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

To reach the joint renewable energy goal each EU country has set their 2030 targets within their National Energy and Climate Plans (NECP) [10], ranging from 10% in Malta to 49% in Sweden, with an average 22% share in the final consumption among all 27 states. Yet, due to the recent introduction of the European Green Deal these targets are to be updated, as in order to achieve the planned climate-neutrality by 2050 a reduction of the core indicator—the greenhouse gas emissions—needs to reach 55% by 2030, thus requiring changes in the corresponding elements of the energy sector. In terms of the share of renewable energy in the final consumption the European Commission expects the target to increase from 32% to 38–40% at the EU-27 level [11].

Such ambitions will require intensified efforts from each EU member state, yet some of them are already struggling to reach the first stage levels—the ones set for the 2020 within the initial Renewable Energy Directive (RED I) and corresponding National Renewable Energy Action Plans. Our analysis of the current progress toward achieving the 2020 national renewable energy goals shows (Figure 1) that by the 2019 (more recent data is still not available [12]) nine countries were behind the schedule. The largest gaps were manifested by France (5.8 p.p.), the Netherlands (5.2 p.p.), Ireland (4 p.p.), and Luxembourg (4 p.p.). Among these countries is also Poland being 2.8 p.p. short of the 2020 goal, while the 2030 target of 21% of renewables in the final energy consumption implies the need of additional 6 p.p. to reach it. Future updates due to the European Green Deal will increase this obligation further.

**Figure 1.** Actual (2019) and targeted (RED I by 2020 and RED II by 2030) shares of renewable energy in gross final energy consumption in the EU-27 countries. Source: own compilation based on [10,12,13].

These expectations will require intensification of efforts from both Poland and other EU countries. Overall, the pace and efficiency of changes in the energy generation structure are determined by various economic factors related to the possibility of financing and conducting the modernization of energy infrastructure, both at the level of energy suppliers and consumers. However, apart from economic factors, non-economic factors of a social and organizational nature also deserve attention. This differentiation is further deepened by regional peculiarities and varying levels of socio-economic development, thus leading to forming of permanent leaders and outsiders among regions, thus deepening the differentiation further due to occurrence of a system archetype [14] called "success to the successful", having an adverse impact on peripheral regions.

Therefore, the article aims to define the existing gaps in renewable energy generation across districts of Poland and to broaden the knowledge about the combined role of regional innovation systems, networks of enterprises and institutions in the process of knowledge and technology transfer and diffusion. It is put in the context of renewable energy generation development taking into account specific conditions of four outlined types of regions in regard to their energy generation capacities and further generation potential. The general theoretical framework is based on the theory of regional innovation systems [15,16], social networks [17] and the concept of "gatekeepers of knowledge" [18]. While this study is based on the case study of Poland, it helps to deliver new knowledge about the processes of renewable energy development typical for other countries as well, especially in the Central and Eastern Europe.

The need to intensify the further spread of technologies and their implementation in various regions arises not only from the necessity to increase the share of renewable energy generation to achieve the set targets. Decentralization of energy generation has also been emphasized upon by the RED II [9] stating that "the move towards decentralized energy production has many benefits, including the utilization of local energy sources, increased local security of energy supply, shorter transport distances and reduced energy transmission losses. Such decentralization also fosters community development and cohesion by providing income sources and creating jobs locally". It is also supporting the transformation toward ensuring of local and sustainable energy access [19], and minimizing possible energy access risks.

The conducted research revealed that in the diffusion of technologies and innovations in the field of renewable energy generation, the flow of knowledge and information is important, as it helps to raise awareness about the necessity of transformation toward renewable energy, as well as simplifies the access to innovational solutions and technologies. Such knowledge relates, inter alia, to the general characteristics of renewable energy sources, which are the resources obtained within natural processes that are not depleting limited natural supplies and are an alternative to conventional non-renewable energy sources. This knowledge is relatively well communicated and understandable to the majority. However, it can be difficult to understand the process of conversion between energy sources and how to engage the transformation in practice. As technologies and solutions constantly develop based on ongoing scientific research, there's a necessity to make this information more accessible and understandable. Currently, nine different sources of renewable energy are used in Poland, the main ones being the solid and liquid biofuels, as well as the wind energy (Figure 2). It is also worth mentioning that the structure of renewable energy generation in Poland is not yet highly diversified and differs significantly from the corresponding structure of the European Union.

**Figure 2.** Structure of generated renewable energy by types of installations in Poland in 2019. Source: [20].

There are various factors causing the existing disproportions in the presented structure of renewable sources of energy. These include, among other, the state support and permit systems, investment costs, local conditions (e.g., territorial, infrastructural). Yet among other crucial factors are also knowledge level and rate of diffusion of innovations and technologies. When looking at regional level, the energy generation there takes place under specific conditions depending on particular region, which could be unbeneficial in case of unfavorable economic and social conditions. This is especially visible in case of peripheral areas, which are characterized by a number of negative phenomena caused by weak economic and social structure, low population density, low level of human capital, low infrastructural development, lack of available and adequate financing sources for implementation of new advanced technologies. Therefore, such areas require a separate approach in the context of renewable energy generation development. In the process of developing the energy innovations, apart from traditional factors related to the development of technologies and their financing, non-economic factors related to social relations and knowledge-flow networks are equally important. In the literature on the subject [21,22], the concept that has already gained a large group of supporters and constitutes the theoretical framework for the implementation of innovation and the flow of knowledge is the existence of regional innovation systems. So, what purpose can they serve in regard to renewable energy generation?

The knowledge and practical skills in the field of renewable energy generation are often developed and shared within limited scientific environments or enterprises involved in production of dedicated equipment. The knowledge gap arising from this fact is not the fault of these professional communities, yet is appearing due to technical limitations, complexity of issues and solutions, and lack of direct networking platforms involving stakeholders of various types. This knowledge gap is among the reasons why the rate of implementation of new technologies in the economy is too slow and falls behind the expected levels. Lack of knowledge about technologies and benefits, not only economic, but also environmental and social, is a significant barrier to sustainable development. Proper knowledge flow and its diffusion in various socio-economic environments can significantly accelerate development processes. One of the concepts that explains the mechanisms of creating and diffusing new technologies is one of the regional innovation system [23]. Its main underlying mechanism are the networks of relations within which their stakeholders exchange knowledge and cooperate in various projects. This concept embraces a multi-disciplinary approach, enabling to understand not only the role of technology in energy generation, but also social and policy conditions that are necessary to boost the processes of its implementation, especially the knowledge diffusion based on social networks and relations.

The emphasis on the importance of intensification of renewable energy generation results from a paradigm shift from the classic perception of energy policy [24] to its perception in terms of sustainable development. While concentrating the following analysis on the energy policy and its implications at local level, it is emphasized by the authors that this approach is not derived from the classic energy policy, but the so-called sustainable energy policy [25], being an overall long-term improvement of social welfare by striving to maintain a balance between the following: energy security, satisfaction of social needs, competitiveness of economy, and environmental protection. Thus, it is more than just ensuring the energy supply. There is a need to balance other crucial socio-economic elements, the condition and quality of which may significantly affect the conditions and the existing potential for development of renewable energy generation.

To conclude the introduction, the article consists of eight sections. Following the Introduction, Section 2 delivers information and literature review regarding peculiarities of renewable energy generation development in Central and Eastern EU countries. Section 3 explains the method used to carry out the analysis of regional renewable energy generation gaps in Poland, substantiates the use of own typology of regions according to their renewable energy generation potential, provides information about the data sources and peculiarities of approach in terms of regional dimension. Section 4 presents the research results, including: (a) an analysis of existing renewable energy generation capacities across

districts of Poland aimed to understand the occurrence and scale of gaps at regional level, (b) an analysis of four outlined types of regions in regard to their renewable energy generation capacities, (c) recommendations for renewable energy development policy across the four outlined types of regions based on the influence of regional innovation systems and potential knowledge transfer. Section 5 covers the substantiation of regional innovation systems, social networks and knowledge transfer from the standpoint of stimulation of regional renewable energy generation in regions demonstrating energy peripherality and gaps, which is based on the literature review providing its synthesis and own arguments. Section 6 covers discussion between the obtained results and other studies, while the Section 7 is devoted to conclusions, implications of presented research and other possible research directions.

#### **2. Peculiarities of Renewable Energy Generation Development in Central and Eastern EU Countries**

Renewable energy sources (wind, solar, hydroelectric, ocean, geothermal, biomass and biofuels) are alternatives to fossil fuels and contribute to reducing greenhouse gas emissions, diversifying energy supply and reducing dependence on uncertain and unstable fossil fuel markets, especially oil and gas. Global development dynamics of energy generation from renewable sources in recent decades indicate that combined they are the fastest growing exploited source of energy. This is clearly visible in economies of such countries as USA, India or China. For example, the key slogan for current India's economic development is "Go Green", which is actively implemented through development of renewable energy generation, zero-emission public transport, other "green" technologies [26].

Yet while the development of renewable energy generation is becoming a global trend, there are differences between countries and macroregions due to specificities of socio-economic past and present development. The report "Global trends in renewable energy investment 2020" indicates that on a global scale, investments in the renewable energy sector in developed economies increased by 2% in 2019 only (compared to the previous year)—to USD 130 billion. At the same time, it is stressed that there were sharp increases in outlays in the USA, Spain, the Netherlands and Poland, and big falls in the UK, Germany, Australia and Belgium [27].

Renewable energy resources and the level of their utilization should be assessed primarily through the prism of a country's energy supply and demand. Consequently, the concept of a renewable energy resource is a purely economic concept and is derived from the function it delivers. And it should be remembered that the amount of renewable energy supply may increase according to the changes in the energy needs and along the growing knowledge and technological possibilities of its conversion into exploitable energy. Although on the EU-27 scale the share of renewable energy in gross final energy consumption has increased from 9.6% (2004) to 19.7% (2019) [12], it is precisely the above-mentioned aspects that can be indicated as the reasons why the "renewable energy revolution" [28] takes place at a different pace in different countries. The use of natural resources for energy generation depends on many factors that could be categorized collectively as: economic, social, legislative, and—to a lesser extent—technological [29]. Although renewable energy generation will definitely be one of key development elements of future long-term EU strategies, their implementation to an equal extent in all EU countries will always be a difficult task due to differences in national priorities and conditions. In Western Europe, the focus on decarbonisation, slowing the climate change and building a single energy market is well underway, while in countries of Central and Eastern EU, where large share of energy is still derived from conventional fuels, a rapid transformation replacing them with renewable energy sources will be more difficult.

The EU member states, in the name of the common good and to limit the climate change can support one energy source at the expense of abandoning other. Yet to implement such choices key factor is the social support. Therefore, an awareness of climate change challenges facing all economic sectors is an increasingly discussed issue among the academic circles and general public, which is the case in the European Union countries, and

especially those in Central and Eastern Europe. Within these debates researchers emphasize the importance of natural, spatial [30], technological [31], social, and political [32,33] conditions for the development of renewable energy generation. Researchers indicate potentials for such generation and determine the possible development paths—both renewable energy generation in general and broken down into specific types of renewable energy sources, also taking into account the interrelationships between sources that can be used for generation of electricity, heat and transport fuels [34].

In the literature it is indicated that countries of Central and Eastern EU have substantial problems in steering the process of setting and achieving long-term energy policy goals due to the short-term way of thinking, characteristic for countries undergoing economic transformation [35]. Not without significance are the past experiences of communism common for the countries of Central and Eastern Europe, which still manifest specific impact on local development conditions, including relatively higher corruption, low trust in political elites and innovative solutions [36]. For example, in Poland, on one side, a strong political force is aiming to keep the traditional framework in the energy sector (e.g., large-scale generation, use of coal), and on the other side, there is an understanding of alternativeless need to intensify the shift to renewable energy generation, which is not only an important element of the global climate protection movement and part of EU policies, but is also cheaper, more efficient and increases local energy security. Yet it is indicated that level of participation of the public in creation of the energy policy is still not sufficient, which may cause the deficiency in development of the prosumer model of renewable energy generation [37]. These issues overall impact the support for renewable energy generation and implementation of policy measures aimed at such development, as well as cause implementation of support mechanisms to be highly volatile, thus making this sector relatively less attractive for investors and entrepreneurs. Existing barriers increase investment risks, which translate directly onto the costs of energy obtained from these sources, which could be higher compared to energy obtained from conventional energy sources. It is also indicated that renewable energy generation technologies are characterized by an uneven pace of energy supply over time or even intermittent operation process, meaning their generation level could be at times inconsistent with the level of energy demand. At the same time, researchers emphasize that the need to take action is forced both by international public obligations in the field of climate change mitigation adopted by all the EU countries (including the Central and Eastern ones), as well as economic, social and environmental considerations, which results in the necessity to undertake costly investments not only in the energy generation sector, but also in energy transmission infrastructure.

#### **3. Materials and Methods**

According to the legislation in Poland [38] a renewable energy generation installation is an installation that constitutes a separate set of devices used to generate energy, described by technical and commercial data, in which energy is produced from renewable energy sources, or construction facilities and equipment constituting a technical and utility unit used to generate agricultural biogas—as well as an energy storage connected to this unit, including an agricultural biogas storage facility.

While the sources of renewable energy vary, generated output can be converted into either electrical or thermal energy. This article, due to data limitations, deals strictly with generation of electrical energy, thus it is meant across the article whenever generation of energy is mentioned.

Processes of innovation and technology diffusion are conditioned by spatial factors and depend on peculiarities of local socio-economic systems. Due to this, an analysis of spatial allocation's differentiation regarding existing technological capacities for generation of energy from renewable sources was carried out in relation to the regional energy demand expressed by a number of people living in a given administrative area. Presented herewith study aims to understand the differences between the regions in development

of renewable energy generation capacities and define what mechanisms determined this process. Description of the research stages adopted for this study can be found in Figure 3. First, a literature review was carried out to explore the renewable energy generation specificities across Poland and define what factors could influence decisions to undertake the development of such generation capacities. In the next stage (the empirical part), a spatial analysis of allocation of renewable energy generation capacities was carried out. It was conducted based on indicators assigned to individual districts in accordance with the "Jenks natural breaks optimization" method [39] and presented on maps. This method is used to present heterogeneous data sets as it aggregates analytical units into groups with similar values. Grouping of values in different classes is carried out by the function aiming to minimize the mean deviation of each class from the mean class, while maximizing the deviation of each class from the mean of other groups. The next step was to develop a typology of regions based on the relationship of the two aforementioned datasets. The first one represented the aggregate index of technological capacity, while the second one represented the population quantity in a given area. Understanding the occurrence of districts manifesting energy gaps and energy peripherality was the basis to go deeper into research of regional innovation systems (RIS) and their possible influence upon intensification of renewable energy generation development. The regional innovation systems therefore, constitute the theoretical framework for considerations and explain such phenomena as social embedding of innovation processes and institutional relations, which play an important role in knowledge and technology transfer processes.

**Figure 3.** Algorithm of the performed research. Source: own substantiation with the use of [39].

In terms of the covered types of renewable energy sources (RES), a region's relative renewable energy generation potential depends mainly on the capacity of generation installations based on renewable energy sources in relation to the energy demand expressed by number of residents in a given region. Large disproportions in this relation cause regional energy surpluses or deficits (energy gaps), meaning in some regions an imbalance between supply and demand for energy is present. In order to better recognize this phenomenon, statistical data was collected about the regional capacities of installations aimed for renewable energy generation, utilizing the following:


Therefore, the aggregate energy generation capacity from renewable sources (EGC) is calculated as the total of generation capacities of all available technologies:

$$BG\_i + BM\_i + PVA\_i + WIL\_i + WO\_i + ITPO\_i = EGC\_i$$

where:

*i*—spatial unit number,

EGC—energy generation capacities from all renewable sources.

Due to existence of diversified technologies for electricity generation the data on different types of renewable energy installations is derived from various sources and covers those entities and individuals which have: a license to generate electricity, an entry in the regulated activity register kept by the President of the Energy Regulatory Office [40] (register of small installation energy producers [41]), an entry in the regulated activity register kept by the General Director of the National Agricultural Support Center [42] (register of agricultural biogas producers [43]), as well as micro-installations generating electricity under the "certificate of origin" system, the guaranteed feed-in tariff system, or the auction support system [44]. The main source of data used within the study were those published by the Central Statistical Office in Poland (GUS), additionally some of the data was obtained from available statistical and scientific publications.

The regional renewable energy generation capacities are presented in the study at a district level, which in Poland are referred to as powiat, being units of the second-degree state administrative division, each consisting of several municipalities (gmina) and having its own administrative body. The total number of such districts in Poland equals 314.

In the scientific literature it is said that deficits and problems in the functioning of regional innovation systems can be related to the conditions that prevail in specific types of regions, such as peripheral regions (facing organizational thinness), old industrial areas (facing cognitive blockades) and some metropolitan regions (facing fragmentation of interactions and networks) [45]. It should be noted that in many cases the problems of regional innovation development are in fact similar, but there may be some dominant innovation problems specific to a given type of region. Therefore, the authors propose four types of regions that differ in terms of population numbers and renewable energy generation capacities.

In the Figure 4, the *X*-axis holds two types of population quantity expressed through the number of inhabitants and the *Y*-axis represents the renewable energy generation capacity. The four quarters are constructed on the basis of the average results of assessments of these two parameters. The average number of inhabitants for the collected data across the regions equaled 82,185 people, while the average renewable energy generation capacity was at the level of 28.5 MW. These values are the cut-off points that determine the energy region types.

**Figure 4.** Outlined region types according to the energy/population parameters. Source: own substantiation.

The research questions to be explored within the article include:

• What disproportions occur between regions concerning renewable energy generation, which regions can be considered peripheral in this regard and which manifest existence of technological gaps?


#### **4. Defining the Renewable Energy Generation Gaps at Regional Level**

Determining the capacity of renewable energy generation installations and the population quantity in districts allowed to develop spatial distribution of energy generation from available renewable sources across Poland. Results (Table 1) revealed that overall energy generation from renewable energy installations within the 2005–2020 increased over ninefold. The quantity and capacity of solar energy installations have shown the most dynamic growth rates. State support programs and subsidies encouraging to invest in such installations are a key driver [46] and a clear long-term state policy toward development of renewable energy generation sources is of high importance [29].

**Table 1.** Dynamics of installed renewable energy generation capacities within 2005–2020 in Poland [in MW].


Source: Energy Regulatory Office in Poland.

In-depth analysis of the collected data split by the types of renewable energy generation installations in Poland is presented in Table 2, while Figure 5 presents their density distributions.

**Table 2.** Descriptive statistics for the analysis of renewable energy generation installations in Poland.



**Table 2.** *Cont.*

Notes: \* More than one mode exists, only the first is reported. Source: own calculations.

**Figure 5.** Density distribution of renewable energy generation installations in districts of Poland in 2020. Source: own calculations.

On the national scale in Poland, among the analyzed types of renewable energy generation capacities the highest density is present in case of installations for solar energy generation, which are located mainly in the north and east of the country (Figure 6). Districts located in the Pomorskie region (north-western part of the country, with access to

the Baltic Sea coast) are national leaders in their construction. In turn, the areas of central Poland are showing the lowest densities or even lacking such installations whatsoever.

**Figure 6.** Renewable energy generation capacity by types of installations in districts of Poland in 2020. Source: own calculations and presentation.

It should be noted that the renewable energy installations are overall scattered across the nation, and such approach is one of the pillars of state energy policy. The purpose of having decentralized energy sources is to provide energy supplies to less urbanized areas and rural areas, as well as to guarantee sustainable local development of those areas. The main reason for the development of decentralized energy sources is the technological progress, which contributes to the reduction of costs of energy generation from renewable sources, as well as the possibility to utilize energy resources available locally.

Figure 7 presents the combined data about renewable energy generation capacities for installations of all analyzed types, while also revealing the distribution of population among the districts, which served to proceed with typologization of regions.

**Figure 7.** Renewable energy generation capacity of all installation types combined (**left**) and population (**right**) in districts of Poland in 2020. Source: own calculations and presentation.

The conducted analysis made it possible to identify four types of regions. Their spatial arrangement across the country has a mosaic pattern (Figure 8). The most numerous are the energy periphery regions (Table 3), which can be described as problematic regions with low energy generation capacity, while also manifesting low population numbers. And as the research conducted in Poland shows [47], the low population is generally accompanied by overall low socio-economic development and deficiencies in infrastructure. This also concerns the energy network infrastructure, which hinders and sometimes even blocks possibility for the development of new energy sources. Low level and quality of internal factors (both traditionally understood as soft factors) are noted here. Districts of this type are characterized by a low level of key conditions essential for the development of innovation. This is referred to in the literature as organizational thinness [45]. The problem of the periphery regions is also the underdeveloped network and connections of specialized knowledge providers, such as universities and research organizations [48]. This type of region occurs numerously throughout the country, but is especially highly concentrated in the north-east and mid-west of Poland. In terms of demographic determinants, the situation in these areas can be assessed as a range from bad to average. They are characterized by a negative migration balance and outflow of local residents, as well as by struggle with the problems of ageing population. Residents in these regions are primarily employed in small-scale agriculture, and have lower average education degree compared to residents of other regions [47]. Compared to Poland in general, these areas are the least economically developed, and the phenomenon of energy poverty occurs to a high extent in these regions.

**Figure 8.** Spatial distribution of districts according to outlined types in 2020. Source: own calculations and presentation.



Notes: \* More than one mode exists, only the first is reported. Source: own calculations.

The second most abundant type are the regions with energy gaps, which boasting a relatively high population are simultaneously characterized by low renewable energy generation capacities. These types of districts are located mainly in central and southern Poland. It should be noted that this type of region could transform rather quickly into the type with sustainable energy potential. This may be due to the existing population potential, since rural areas of this type are showing a positive demographic trend and are a migration destination [47], thus additionally stimulating their development. Areas of this type are often located near large cities that determine population development trend, but at the same time the low renewable energy generation capacity may indicate insufficient development of infrastructure networks (including energy networks). Unlike the periphery regions, they face the reverse problem of over-clustering because they are over-specialized in mature industries hit by decline [49]. This type of regions often has a highly developed and specialized system of knowledge generation and diffusion; however, their problem is too much focus on traditional industries and fields of technology (e.g., the region of Silesia) [50]. With regard to functioning networks of relations, a key feature of the old industrial regions is that they suffer from various forms of "lock-in" which significantly block the development potential and the possibilities of diffusion of innovation and knowledge. Such blockades are a consequence of overly rigid networks established between enterprises and the policy, and links between public and private entities, which hinder the process of industrial restructuring. However, these districts have a substantial positive potential for the development of prosumer energy, as the population with relatively higher income levels is more eager to install renewable energy installations [51]. Residents of areas described as regions with energy gaps are relatively well educated. These are people who maintain permanent contacts with the city—mainly by being employed there. It is possible that the environment these people dwell in, contacts with other educated and highly aware citizens positively affect their pro-environmental attitudes, which in turn leads to growing popularization of the prosumer model of energy generation in the areas of their primary residence.

Another type are the regions with energy surpluses. This type is characterized by low population potential and high energy generation potential—which is not used due to low population density. Spatially, such districts are located primarily in the north of the country—mainly in places where wind energy generation is being developed. Nevertheless, this type is also highly dispersed nationally. It can be seen that the surplus of energy results from the specificity of the sources located there. The energy generation capacities are not always located at the densely populated areas. The key task of such regions is to create energy transmission grids with regions of high energy needs. Financial resources and systemic solutions are needed so that these regions could flawlessly transmit energy. There is a lack of organizations that have commercialized this energy generation potential and there are no institutions connecting the regions.

The last and least numerous type are the regions with sustainable energy potential, which are characterized by high population numbers and high renewable energy generation capacities. Districts of this type are highly dispersed throughout the country. However, the sequence of communes running from Pomorskie to the central part of the country is quite characteristic. These regions are the least problematic because there is a balance between their needs and generation capacities. Yet political decisions, unfavorable for further development and the lack of continuation of the energy policy may pose a threat. Energy generation leaders in these regions block small producers, which makes it difficult to implement the idea of decentralized energy generation based on individual prosumers.

In the light of the differences in outlined types of regions an important issue is to understand possible implications that may have a systemic character and hinder the potential development of renewable energy generation. For this purpose a typology of systemic problems [52] has been combined with types of regions outlined in this study (Table 4). While the types of regions differ according to their renewable energy generation potentials, all four types may encounter mild or severe potential systemic problems, which might either slow down their positive development, or block possibilities of such. Again, an intensive transfer and wide diffusion of knowledge in this matter could serve as an important factor ensuring minimization of potential negative implications.

**Table 4.** Potential systemic problems in regions depending on their renewable energy generation.


Source: own substantiation utilizing the typology of systemic problems from [52].

#### **5. Regional Innovation Systems, Social Networks and Knowledge Transfer**

The concept of regional innovation system (RIS) is popular among scientists of various disciplines as its theoretical framework responds to the needs of researching the phenomena and mechanisms related to the emergence of innovation and knowledge transfer [45]. The main idea behind the RIS is that the innovation efficiency in the economy depends on the innovative capabilities of enterprises and research institutions, and on how they interact with each other and public institutions [53]. The specificity of the concept lies in the fact that it shows overlapping dimensions, i.e., broadly understood institutional infrastructure and production system, and then explains the mechanisms of relations that arise between them based on established rules and regional policy. In this part of the study, authors search to answer what theoretical perspective the concept of RIS originates from, what are its main components and mechanisms, and whether there are different forms of RIS in the context of regional diversification of the periphery center.

The concept of RIS was created as a result of evolution of views on the functioning of the national innovation system (NIS), which was described in the works of Edquist [54], Lundvall [55], and Nelson [56]. It should be noted that it is quite difficult to identify the differences between NIS and RIS. The rationale for the emergence of the RIS from the general concept of NIS was that researchers wondered to what extent regions differ from one another in terms of their potential and processes that take place in the creation and absorption of innovation. It became the reason to propose a new concept, which to a greater extent takes into account the regional disparities of potentials for the creation of innovations. One of the first attempts to define and describe the concept of regional innovation systems can be found in Cooke et al. [15] who define the RIS as a system in which companies and other organizations are systematically engaged in interactive learning through an institutional environment characterized by embeddedness. In addition, Asheim and Isaksen [16] noted that a (regional) innovation system consists of a production structure (techno-economic structures) and institutional infrastructure (political-institutional structures).

In the definition of the concept of a regional innovation system, there are three important aspects that are key to its understanding. First is the expression "interactive learning", which means an interactive process by which knowledge is transferred, then combined and, as a result, constitutes a knowledge base as a shared resource of various entities cooperating in the system. Knowledge is the basic factor used not only in the process of creating innovation, but also in the process of absorption of innovation/technology and building cognitive, organizational and social closeness. Second, the term "environment" being an open territorial complex that includes principles, norms, values, and human and material resources. It is a set of territorial conditions which combined create a potential for the functioning of a system specific for a given area. The third aspect that definitely distinguishes the discussed concept is paying attention to social closeness. Economic relations are to some extent always embedded in a social context, while social ties or relations influence economic performance [57]. Social closeness is related to the term "embeddedness" which covers all economic and knowledge creation processes, and then its duplication in business environment and beyond. The process of learning and absorption of innovation is often based on trust, therefore social relations facilitate the exchange of tacit knowledge, which is by nature more difficult to communicate, exchange and trade through markets [58].

The concept of RIS assumes that innovation is a process in which enterprises use both internal and external resources of a material and institutional nature. It should be emphasized that the functioning of regional innovation systems depends not only on knowledge resources created by enterprises and institutions, but also on the strength and structure of relations, created networks, which are the platform of cooperation with the environment. Innovations do not arise in isolation and cannot thrive solely based on given enterprise's internal resources, but are rather a result of synergy of numerous factors and processes. The environment mentioned earlier can therefore be perceived as a network of entities and institutions that form the framework for innovative activity and interactive learning. Thus, the interaction between educational organizations, which can be defined in terms of knowledge and information flows, investment flows, networking and other partnerships, is the most important process driving the evolution and strengthening of RIS [53]. In conclusion, RIS is primarily a social system that involves systematic interactions between different groups of private and public sector institutions and individuals in order to increase localized learning opportunities in the region.

The innovation system requires defining its main components, i.e., institutions that play an important role in the innovation process. Lundvall [55] lists the basic elements of such system, which are: internal organization of enterprises, relations between enterprises, role of the public sector, institutional structure of the financial sector, research and development intensity, and research and development organizations. In general, the main

elements encompassing RIS are enterprises, institutions, and the knowledge infrastructure and innovation policy.

An innovation-oriented policy is an important regulator of the processes that take place in the regional innovation systems. Its direction and scale of activities increase, among other things, the possibilities of learning and diffusion of knowledge. As practice shows, the optimal level to implement an innovation policy is one of a region, which was confirmed in the policy carried out by the European Commission [59,60]. The genesis of these policies dates back to the 1980s, when a group of OECD experts developed the concept of a dynamic approach to international competitiveness as an alternative to a static, cost-based view of the theory of international trade and competitiveness. The concept developed at that time assumes that international competitiveness can be achieved by promoting learning and innovation development in societies. Also in other dimensions, the approach to innovation systems represents an important theoretical and political progress. Identifying innovation as a key factor of economic growth emphasizes the role of interactive learning processes between multiple entities and organizations [61]. Such RIS policies aim to improve the interaction and collaboration between the knowledge infrastructure, companies and institutions. Moreover, these policies respond to individual and collective innovation needs. In other words, strategies are developed to support the endogenous institutional capacity of regions by encouraging the diffusion of technology on a regional scale [62]. Innovative policy tools typically include: managing the scientific knowledge base; providing financial incentives for innovation efforts, technology dissemination policies and initiatives; promoting programs and companies leading the implementation of new technologies; and the creation and maintenance of intangible assets and legal regulations that favor innovation and technology transfer.

RIS concept distinguishes four main internal mechanisms that explain the efficiency and success of the system, being the: interactive learning, knowledge generation, proximity, and social embeddedness. At a heart of the concepts of RIS and knowledge transfer is the concept of embeddedness, which is based on relationships and social networks and requires an understanding of the institutional and cultural context [55]. The concept of embedding appeared in social theory and works of James Coleman [63], which is the main propagator of the concept of social capital theory. According to this theory, the concept of rooting refers to resources embedded in the structure of social relations (networks). In social capital theory, the concept of "social embedding" describes a situation in which economic activities and behavior are related to or depend on non-economic institutions and activities such as culture, social networks, politics and religion [17]. The social structure is made up of connections with social networks, the key element of which is shaping of this rooting, as well as cohesion, integration and social support [64]. These are the features that are not subject to market rules, cannot be duplicated or sold, but are crucial for interactive learning [65]. From the perspective of diversifying development potentials, embedding occurs in regions where there is a significant concentration of enterprises and institutions, a high degree of shared social and cultural values, and various resources that can be used to generate new production and processes.

Within RIS, embedding concerns mainly the relationship between interactive and collective learning and the nature of knowledge exchange between enterprises and their institutional environment that supports innovation processes and knowledge transfer. It follows that networks of social and organizational relations constitute a key dimension of embeddedness. For shaping the policy of innovation and diffusion of knowledge, it is important to indicate which network structures are created under the RIS in regions with specific conditions influencing the development of innovations, as well as understanding who are the gatekeepers of knowledge, what are the mechanisms of information transfer and what are the abilities of the potential recipients to the knowledge.

In knowledge-based economies, innovation is considered to be the key driving force behind economic development [66]. Nowadays, they can rarely be developed by single entities or individuals. Their creation and success require the activation of the broadly understood innovative potential, located in the private sector of the economy, but also the one accumulated in the public and civic sector, so that as a result it is possible to engage the potential of creativity and innovation on a mass scale [67]. For this reason, intangible assets, in particular relational capital, play a special role.

This capital, apart from human capital (competences, education) and structural capital (structure/organization), is one of the components of intellectual capital [68]. It is defined as resources related to interpersonal relations, the ability to establish and maintain close and lasting relationships, building one's own social network [69]. Relational capital enables the creation of a network of contacts and long-term cooperation relations. The high level of this capital influences the connection of entities operating in the networks. It also affects the quality of information flow between network links and joint activities undertaken by all or only some of the entities [70].

These "social networks" contain two important components. The first is the network, which is essentially considered a structure formed by entities (primarily actors/entities) and their connections. The social nature of these connections, taking the form of interactions, relationships and ties, is the second component. Functioning in networks allows you to reach various resources through the exchange of knowledge. It enables the acquisition of external knowledge and combining it with individual/organizational and tacit knowledge. Nowadays, in a complex environment requiring a variety of reactions and stimuli, even large companies and organizations find it difficult to gather all competences and skills in one place [71]. An important element is also the interpenetration of different areas of knowledge. In addition, collaborative networking enables greater freedom and security through sharing experiences and sharing risks. Some researchers also argue that network structures can accelerate trust building in R&D cooperation, which typically requires mutual disclosure of knowledge related to competition [72,73].

On the one hand, networks enable the flow of knowledge based on direct relations, and on the other hand, they can contribute to the exchange of knowledge through indirect connections. In the case of indirect knowledge exchange, innovation brokers and gatekeepers of knowledge play an important role. Brokers are network actors that transfer knowledge between organizations that are not directly related [72]. They play the role of an information intermediary between information resources and people/organizations that need information. On the other hand, gatekeepers of knowledge absorb knowledge scattered on a global scale and introduce it to innovation processes—both at the regional and local level [66,71,74]. Their tasks, according to Wesley Cohen and Daniel Levinthal [75], are to monitor the external environment and translate technical information into a form that is understandable to local stakeholders. As a result, gatekeepers contribute to the popularization of new ideas and the transfer of new knowledge to the regional and local level [66]. Both brokers and gatekeepers act as knowledge repositories and contribute to the use of knowledge they derive from different contexts [76]. As a result, they do not so much control the flow of information, as influence it, among other by interpreting the message or giving it a specific meaning. In a sense, they decide which information will circulate and which will not.

#### **6. Discussion**

The study attempts to use the concept of regional innovation system to understand the dynamics of renewable energy generation development and the role and peculiarities of energy policy aimed at its development. Scarce attempts have been made so far to explore how the concept of RIS and social networks can support the diffusion of knowledge related to renewable energy sources. Moreover, the research conducted so far focuses mainly on regions with strong centrality features and well-developed economic and research infrastructure. Recent studies, however, suggest paying more attention to deficit and peripheral regions and their determinants for the creation and absorption of innovation and new technologies [77–79]. A particularly important area of research in which there is a knowledge gap are the problems of innovation systems occurring in regions with an energy deficit and peripheral regions with specific development characteristics, which can provide an appropriate framework for shaping energy policy focused on the development of renewable energy sources.

In order to understand the mechanisms underlying the appearance of regions characterized by energy gaps and energy peripherality, one should refer to the scientific paradigm related to the systemic nature of innovation [80]. This paradigm explains that the speed, direction and success of innovation processes are highly influenced by the environment, i.e., the regional innovation system in which innovations arise. Such a system as a complex structure of various institutions and their relations and principles of functioning, may encounter many emergency situations that hinder the processes taking place within it. Understanding these problems will help to better understand the occurrence of regional disproportions in the rate and level of investment for installations related to the production of renewable energy.

The main feature of large technological systems, including energy systems, is a strong interconnection with the economic system [81]. This dependence means that the transformation of the energy system will affect all elements of a sustainable economic system and requires an excessively high modernization effort, which many economies have not dealt with so far [82]. Literature review shows various theoretical and practical examples of problems related to the functioning of systems, such as: problems with the market structure, infrastructure problems, institutional problems, problems with interaction and problems with opportunities and local potential [52]. The systemic context allows, first of all, to identify directions of policy and public support, and to indicate areas with deficiencies in energy innovation and gaps in knowledge about them.

The new technology may face problems resulting from the market structure and competitive substitutes, which may be cheaper than the introduced innovations or have low utility when, for example, there are no externalities in the network. Moreover, when some actors dominate and control the market, the customer selection processes are limited [83]. Practical examples in Denmark show that in case of renewable energy generation installations, the small-scale wind energy generation technologies have been implemented successfully [84], as opposed to large-scale energy projects such as biomass [85], gasification and heat pumps [86], which in practice hampered the dissemination of the technology.

Infrastructural problems may concern equipping the region with physical facilities necessary for the functioning of society or enterprises in economic structures. These are, for example, electrical energy, natural gas transmission/distribution networks and communication networks such as high-speed ICT infrastructure and highways. Another dimension of infrastructural problems is equipping the region with a physical knowledge infrastructure, which includes highly specialized buildings (laboratories and research facilities) and equipment, as well as intangible infrastructure related to scientific and applied knowledge. The implementation of investments related to RES generation installations requires the transformation of large technical systems, such as the energy system. This is associated with high investment costs for the expansion of new infrastructure and coordination problems, and the entire process often requires government intervention [87].

Institutional problems relate to institutional mechanisms that may hinder innovation processes in the region. Institutions are the main constituent of innovation systems, and the institutional context defines this system and provides a structural framework. Formal institutions are consciously created and are characterized by clearly articulated and written rules of conduct, while informal institutions function as established rules of the game rooted in local social and cultural structures. Together, these two dimensions of institutions create the environment in which companies, knowledge institutions and the government itself are embedded [87]. In practice, institutional problems caused by instability in regulations and subsidy systems are often encountered. Once adopted, activities and support programs are withdrawn to be restarted after a few years. Such situations for micro-CHP, wind, PV, biomass and marine energy have been observed in the UK [88] or with solar collectors in Sweden [89]. Another observed phenomenon is the shift in political priorities with

regard to the technology or its application context. An example of such activities is the implementation of solar cells in the Netherlands. A policy was adopted in the 1970s–1980s that focused on countries with the highest theoretical solar energy potential and developing countries as a priority, followed by a sudden increase in interest in PV technology in the 1990s, which due to climate change was also seen as an opportunity for the countries of North-Western Europe. The following years saw a change in policy and PV technology is no longer considered a viable option due to its high cost [90].

Another frequently mentioned problem in the functioning of institutions is the lack of coherent actions between different administrative levels in a given country. An example are biofuels and PVs in the Netherlands, both supported by provincial (regional) governments, while at the national level the government hinders the development and diffusion of these technologies [91,92]. Informal institutions, on the other hand, are responsible for the legitimacy of the implementation of new technologies and their social acceptance and observance through the prism of a given institution [93], in case of new technologies, obtaining legitimacy is often a slow and tedious process.

The functioning of formal and informal institutions often results in problems with network interaction. Actors of the innovation system, such as: enterprises, knowledge institutions, government—all interact with each other, including regarding product development and design, knowledge exchange and diffusion of new technologies. Interference and inefficiencies in the functioning of the network can be caused by either too strong or too weak interactions.

Network failure occurs when dominant gatekeepers and knowledge brokers fail to fulfill their role and consequently fail to provide the required knowledge. The network can also be too closed to external interactions, which means that actors are reluctant to leave the group or let new participants into it. Another situation concerns the imprisonment of relationships and the inability to develop the network, it results from the same costs of such changes as well as the possibility of establishing relationships with new partners. Network failure may also be caused by poor connectivity of network actors with new technologies, which prevents the process of learning, adaptation to new technological developments and innovations. Moreover, little involvement in cooperation in the system may lead to the lack of a common vision of technology development in the future, which in turn may hinder the coordination of research efforts and investments [87].

The company's abilities in the form of the lack of competences and resources to modernize and implement a new technology may also prove to be a significant problem [94,95]. The possibilities of searching for new solutions are significantly limited by the knowledge gaps of enterprises and the long cognitive and geographical distance, which is why they are often not aware of the existing opportunities and do not include innovation in their development vision [83].

Another issue are the interaction problems that affect the dissemination of knowledge in a multi-stakeholder regional system. Networking of different actors facilitates knowledge flows, accelerates technology development, reduces uncertainty and creates demand. Research in this area shows that weak or excessively strong connections and disturbances in the network are a mechanism blocking renewable energy generation technologies.

The case of Swedish producers of small biofuel boilers shows the lack of cooperation within the network and the weakness of the relationship [96]. There are only two or three producers of large biofuel boilers in Sweden, therefore the lack of cooperation may be due to the lack of potential partners. On the other hand, the weakness of relations and connectivity within the system may partially result from an information gap about other entities being potential partners in the region. Another issue is the considerable individualism of small companies, which means that these companies do not want to cooperate and share their knowledge with other companies. In addition, some companies, rooted for some time in local economic and social structures, are reluctant to new entities and create distance instead of building relationships. Summarizing, the case of Sweden shows that cooperation networks within the framework of energy generation technologies

are characterized by poor connectivity and a lack of willingness to cooperate and share knowledge with other companies.

In terms of ways to assess the efficiency of regional innovation systems and their influence upon creation of innovations one of them is to take into account the number of patents. A research [97] was conducted within 194 countries to assess how different renewable energy support policies affect innovation in solar and wind energy generation technologies. This substantial work shows that a more comprehensive portfolio of renewable energy support policies increases the number of patents in the particular field, as well as there is a definite positive impact on patent activity, which is increasing significantly over time along with the growing duration of research programs and achievement of R&D objectives.

A different approach to understanding of opportunities and constraints that are created by the development of renewable energy generation capacities is mentioned by van Zalk and Behrens [98] in the U.S.A. context. Namely, in their opinion the issue of land use in this regard is not in favor of renewable energy development, as "the surface area required for renewable energy systems is greater than that for non-renewable systems, exacerbating existing environmental policy challenges, from increasing land competition, to visual impacts". While this is certainly true and needs to be taken into account, the overall environmental benefits from decreased pollution are still prevailing.

#### **7. Conclusions**

The Polish energy sector is currently facing serious challenges, the currently defined directions of the state energy policy are to a large extent interdependent. With the clear goals to increase the share of renewable energy generation in the next decade intense transformations are needed to reach them, as in the past years its growth rates have been falling behind "the schedule". These can be achieved by simultaneous increase of renewable energy generation shares and by improving the energy efficiency. The later reduces the increase in demand for fuels and energy, contributing to increased energy security, as a result of reducing dependence on imports, and also works to improve the environmental impact of energy by reducing emissions. Similar effects are brought by the development of renewable energy generation, including the use of biofuels and non-pollutant technologies.

However, the sector also faces numerous challenges resulting from, inter alia, permanently high energy demand, inadequate level of development of fuel and energy production and transport infrastructure, high dependence on external supplies of natural gas, nearly total dependence on imported supply of crude oil, and environmental protection obligations, including climate change mitigation. These intensify the necessity to implement decisive measures to prevent deterioration of the economic situation of fuel and energy consumers. Therefore, in order to fully use the energy potential in Poland, it is necessary to use innovations and knowledge, especially expert knowledge. This is of exceptional importance for the energy sector, which operates in a complex legal, economic and technological environment.

In the energy sector nowadays, as in other sectors of economies, one of the conditions for functioning and development is the systemic use of knowledge to solve emerging problems, including creating and facilitating innovations. The fulfillment of this condition requires an incorporation of knowledge into management. The use of knowledge acquired at the local and regional level, within the company, as well as in contacts between various organizations obtained through networking activities can serve the process of gaining knowledge through the exchange of experiences, mutual evaluation of one's models and practices, exchange of views and ideas, and conducting joint experiments [18,70]. Network participants who are in contact with each other ignite discussions and generate new ideas, define stimulants and share experiences. A special role here is played by entities such as knowledge gatekeepers and brokers, which are responsible for providing practically useful knowledge to solve key problems of an organization (this type of knowledge can be defined as informal knowledge or unclassified knowledge), as well as information that contributes to the creation and implementation of innovative solutions. Gatekeepers and

brokers often act as liaisons who influence the creation and connection of various sets of knowledge, both within the company and from external sources. These are people and entities with networking skills, with a high level of social and communication skills.

It is worth noting that while in highly developed, innovative areas both the networks themselves and the entities intermediating in acquiring are quite "dense", their shortage in peripheral areas may be another element hindering the development of innovation. In regional systems, the strive to increase innovation and related entrepreneurship contributes to changes in the functional structure of regions towards strongly developing regional centers and less growing peripheral areas [99]. However, it is commonly assumed that firms in peripheral regions benefit less from local knowledge transfer than firms located in agglomerations or industrial clusters [100]. This is due to the fact that the peripheral regions are characterized by a weaker supply of local knowledge transfer than the key regions. The literature indicates that companies and organizations from peripheral regions can be innovative to the extent that they are able to compensate for the missing possibilities of spreading and absorbing knowledge [101]. It is the collaboration in networks and with intermediaries for acquiring knowledge and skills that is a potential compensation mechanism, as they establish the organizational framework (organizational proximity) that enables interactive learning processes.

To conclude, the conducted analysis on the level of districts has revealed that the energy generation capacities in Poland are spatially unevenly distributed. Most of the surveyed districts are problematic areas which can be defined as energy periphery or energy gaps. These are areas that, regardless of the population and demographic situation, or the level of socio-economic development, are characterized by a low energy generation potential. Numerous densely populated areas are characterized by a scarcity of energy generation installations, thus also represent low energy generation potential. This may indicate the fact that the development of the network infrastructure—in this case, above all, the energy infrastructure—is not keeping up with the influx of people as well as social and population changes. Nevertheless, the investment potential in most of Poland is high. Thus, investment opportunities could be looked for in virtually every area of the market.

The authors focused on the characteristics of Polish regions regarding generation of renewable energy and its dependence on regional innovation systems and knowledge transfer. However, detailed definition of renewable energy generation development in light of local development conditions in various regions requires further study—including, among other, field research. It may be especially valuable to learn about non-economic factors, including relational capital and its role in the processes of energy generation knowledge/technology transfer and collective learning. Another important aspect that relates to regional innovation systems and renewable energy is the issue of existing policies for innovation and energy generation development. This would help to understand if their directions and implemented measures become catalysts for each other and if this accelerates the processes of transformation of the energy generation system achieving synergistic effect.

An interesting aspect for further analyzes could also be a research of the structure of energy generated from renewable sources, yet broken down into its types and specific sources. Investment chances could be analyzed separately, which are needed on every stage of product creation, in this case the generation and distribution of energy. Consequently, this may translate into energy security studies, whether on national or regional levels, either of which is a strategic issue for each country. Production and transmission of energy is an economic bloodstream, which, in addition to the transport system, determines the efficient functioning of the economy. Economic development of all countries depends on access to energy, which is why its sustainable development is so important.

**Author Contributions:** Conceptualization, P.Z., V.K. and D.Z.-G.; methodology, P.Z.; writing original draft preparation, P.Z., V.K. and D.Z.-G.; writing—review and editing, P.Z., V.K. and D.Z.-G.; visualization, P.Z., V.K. and D.Z.-G.; supervision, P.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Energy Self-Subsistence of Agriculture in EU Countries**

**Tomasz Rokicki 1,\*, Marcin Ratajczak 2, Piotr Bórawski 3, Aneta Bełdycka-Bórawska 3, Barbara Gradziuk 4, Piotr Gradziuk <sup>5</sup> and Agnieszka Siedlecka <sup>6</sup>**


**Abstract:** The paper's main purpose was to identify the level and factors influencing the consumption of bioenergy of agricultural origin in agriculture in EU countries. All EU countries were deliberately selected for research, as of 31 December 2018. The research period covered the years 2004 to 2018. The sources of materials were the subject literature, Eurostat data, and IEA (International Energy Agency) data. The following methods were used for the analysis and presentation of materials: descriptive, tabular, graphical, Gini concentration coefficient, Lorenz concentration curve, descriptive statistics, Kendall's tau correlation coefficient and Spearman's rank correlation coefficient. In the EU, there was a high level of concentration of renewable energy consumption in several countries. There was also no change in the use of bioenergy of agricultural origin in agriculture, but the concentration level was low. The degree of concentration has not changed for both parameters of renewable energy over a dozen or so years, which proves a similar pace of development of the use of renewable energy sources in individual EU countries. Higher consumption of bioenergy of agricultural origin in agriculture was shown to occur in economically developed countries, but with high agricultural production. There was a strong correlation between the consumption of bioenergy of agricultural origin in agriculture for the entire EU and individual economic parameters in the field of energy and agriculture. The relations were positive for all economic parameters, for total renewables and biofuels consumption and for agricultural production parameters. Negative relations concerned the total energy consumption and parameters related to the area of agricultural crops.

**Keywords:** renewable energy sources; agriculture; energy policy; energy in agriculture; bioenergy of agricultural origin

#### **1. Introduction**

Preserving the natural environment for future generations is one of the most important goals facing the world [1–3]. This issue was presented at many conferences and discussions at the global and regional level [4–6]. This problem has also been dealt with in the European Union. In December 2008, the Council of the European Union adopted assumptions on counteracting climate change. The EU plan is commonly known as "3 × 20", but there were four proposals [7,8]. According to them, by 2020, the European Union should reduce greenhouse gas emissions by 20% (compared to 1990), increase the share of energy from

**Citation:** Rokicki, T.; Ratajczak, M.; Bórawski, P.; Bełdycka-Bórawska, A.; Gradziuk, B.; Gradziuk, P.; Siedlecka, A. Energy Self-Subsistence of Agriculture in EU Countries. *Energies* **2021**, *14*, 3014. https://doi.org/ 10.3390/en14113014

Academic Editors: Talal Yusaf and Francesco Calise

Received: 5 April 2021 Accepted: 21 May 2021 Published: 23 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

renewable sources (RES) in its total consumption to 20%, and increase efficiency by 20% energy. It was also assumed that the share of biofuels in the total consumption of transport fuels would increase by at least 10%. For individual countries, different target shares of energy from renewable sources in gross final energy consumption have been defined for 2020 [9,10]. Its highest share was expected in Sweden (increase from 39.8% in 2005 to 49.0% in 2020), Latvia (from 32.6% to 40%) and Finland (from 28.5% to 38%). In other countries, the target share ranged from 10% in Malta, to 15% in Poland, to 31% in Portugal. In total, this share was to reach 20% or more in 12 countries, and up to 15% in 10 countries. In the remaining ones, it was lower than 15%. The next challenges posed by the European Union are even more ambitious, as they involve achieving a reduction by at least 40% of greenhouse gas emissions by 2030 (compared to the level from 1990), increasing the share of renewable energy in its total consumption to a minimum of 32% and an increase of at least 32.5% in energy efficiency [11–13]. The European Green Deal published by the Commission has set out a clear vision of how to achieve climate neutrality by 2050. It proposes to increase the EU's climate ambition for 2030 and 2050, a zero-pollution ambition for a toxic-free environment, supplying clean, affordable and secure energy, mobilizing industry for a clean and circular economy, building and renovating in an energy and resource efficient way, preserving and restoring ecosystems and biodiversity, a fair, healthy and environmentally friendly food system, accelerating the shift to sustainable and smart mobility [14].

For millennia, mankind has mostly used natural, reproducible energy sources. These were plants that provide food and fuel, animal products (including oils), and to some extent, water, wind, and especially the sun. Along with the socio-economic development, raw materials obtained from the depths of the earth, such as coal, oil and natural gas, were increasingly important. These are non-renewable resources and their combustion products pollute the environment. In this situation, mankind is forced to look for such energy sources that are constantly recreated. Such sources include solar energy [15], wind energy, water [16], including river currents, sea and ocean waves, nuclear energy [17], energy from biomass [18], biogas or bioliquids [19]. Renewable energy also includes the heat obtained from the ground (heat pumps, geothermal energy), air (aerothermal) and water (hydrothermal energy) [20–24].

The most important feature of renewable energy is its inexhaustibility. In addition, renewable energy sources have a much lower negative environmental impact than conventional fossil energy technologies. Most of the expenditure related to renewable energy is related to the materials and labor needed to build and maintain facilities. However, there are no costs of importing energy [25–27]. The main advantages of renewable energy include ensuring energy security for the future, which means a continuous and uninterrupted supply of energy necessary to run the economy [28]. There is a strong relationship between the level of energy intensity and socio-economic development. Renewable energy also creates additional jobs [20,29,30]. Another advantage of renewable energy sources is their availability. These sources are scattered around the world, making them easily accessible to everyone. Renewable energy reduces the electrification gap between rural and urban areas. As a result, it can affect the development of rural areas [31]. Where connection to the power grid is almost impossible, renewable energy is the most effective solution [32–34]. RES also reduces the negative effects on the environment and health. The emission of harmful substances to the atmosphere is reduced and the carbon footprint is reduced. This reduces the risk to human health as most diseases are related to air pollution [35–37].

Agriculture provides many types of renewable energy. It is easiest to identify energy produced only in agriculture, such as solid and liquid biofuels and biogas. Solid biofuels are defined as any plant material that is used directly as fuel or transformed into other forms prior to combustion. This includes many wood materials produced by an industrial process or supplied directly by forestry and agriculture (firewood, wood chips, bark, sawdust, shavings; sulphite lyes, also known as black liquor; animal materials/waste, industrial waste (renewable) and others solid biofuels). Charcoal is not included in this category. Biogas are gases consisting mainly of methane and carbon dioxide, produced

either by anaerobic digestion of biomass or by thermal processes. Liquid biofuel is primarily biodiesel, mixed or replaced with fossil gas or diesel fuel [38–41].

Agriculture is a significant producer of renewable energy and is considered by policy makers in this respect. Meanwhile, the consumption of renewable energy in this sector must also be encouraged. Ideally, this energy should be produced on farms themselves. The undertaken research topic is important as it shows the other and still underestimated side of renewable energy in agriculture, i.e., its consumption. Thus, the article fills the research gap.

The paper's main purpose was to identify the level and factors influencing the consumption of bioenergy of agricultural origin in agriculture in EU countries. Additional objectives were to define the conditions for the development of renewable energy sources in the EU, determine the directions of changes and the importance of renewable energy in individual EU countries, and present the consumption of bioenergy of agricultural origin in agriculture in EU countries.

Two research hypotheses were formulated in the paper:

**Hypothesis 1.** *The processes of the concentration of bioenergy of agricultural origin in agriculture entails a greater concentration of this energy consumption in the countries that are the largest agricultural producers in the EU*.

**Hypothesis 2.** *The use of bioenergy of agricultural origin in agriculture was closely correlated with the parameters of agricultural production*.

The organization of this paper is as follows: in Section 2, the literature review is elaborated. Section 3 proposes methods to identify the level and factors influencing bioenergy consumption of agricultural origin in agriculture. In Section 4, the results of the research were presented. In Section 5, Discussion, reference is made to other research results that dealt with the relationships tested. Finally, Section 6 concludes this paper.

#### **2. Literature Review**

Agriculture has many functions. In this sector, natural resources are somewhat limited, as is the land stock. Thus, there is a competition between the use of land for food production and energy purposes. The production and use of renewable energy represent a secondary transformation in the agricultural sector [42–47]. The production and consumption of renewable energy may result from the reluctance of farmers to use environmentally harmful fuels and as a way to diversify agricultural production. The biophysical features of the farm are also important, as they determine the investment in the production of renewable energy [48–51]. In addition, for example, the production of agricultural biogas or electricity from agricultural biogas is a regulated activity requiring the registration of energy companies operating in the production of agricultural biogas. The external factor for developing small-scale agricultural biogas plants is the system of subsidies on this account. Financial aid in the EU is granted to farmers for projects to diversify into nonagricultural activities, including agricultural biogas production and energy production from agricultural biogas. Internal factors concern human and financial resources and the level of marketization of agriculture and the agrarian structure in a given area. The size of farms mainly determines the development of agricultural biogas plants. It is easier to obtain the raw material for agricultural biogas production in large farms [52,53]. One way to produce and consume renewable energy in the agricultural sector is to use crop residues from existing crops [54]. Biomass production can, in principle, apply to all types of agricultural products. The potential for energy use is therefore very large [55].

Biofuels can be one of the sources that meet the global energy demand. Their advantage is environmental neutrality [56–58]. Agriculture is one of the most important sectors that supply various forms of biofuels. The production of first-generation biofuels relies heavily on energy crops such as maize and sugarcane. In Europe, biodiesel production is dominant, such as in the USA—of ethanol [59,60]. Second-generation biofuels are made

of cellulose, hemicellulose or lignin. The lignocellulosic raw materials are mainly maize straw, rice husk, wheat straw and sugar cane bagasse [61]. Second generation biofuels can be blended with gasoline, which can be burned in internal combustion engines and distributed via existing infrastructure or engines slightly modified for internal combustion. An example of second-generation biofuel is cellulose ethanol, which is produced biochemically [62]. Third-generation biofuels come from algae biomass. The production of biofuel from algae is usually dependent on the lipid content. Algae are used, among others, for the production of biodiesel [63–67]. Fourth-generation biofuels use inexhaustible, cheap and widely available raw materials to convert solar energy into solar biofuels. The production of photobiological solar biofuel or electrofuel uses the synthetic biology of algae and cyanobacteria [68–70].

Biofuel production systems competing with farmland may to some extent threaten food production, but also increase environmental pressure and affect biodiversity and ecosystem services [71,72]. The production of biofuels by agriculture is part of the concept of sustainable agriculture [73]. On the other hand, agriculture should also use the energy produced in this sector, e.g., biofuels. Then, in a sense, the agricultural sector would supply itself with energy [74,75]. Such activities would also be beneficial for sustainable agriculture and the environment [76,77]. In agriculture, renewable energy can be used for heating and cooling. The problem here is the existence of low-capacity installations that convert this energy and supply the farm directly. An example is the use of biogas, the production of which is unlimited by climatic conditions. Biogas is used in agriculture for cooking and heating. Another possibility is to produce biofuels in small factories on the farm. Then, such fuel can be directly used in agricultural machinery operating on a farm. Optionally, it can be used as a percentage additive to conventional fuel [78–83].

Increasing the use of renewable energy can be achieved by initiating effective policies by governments. Policymakers should encourage domestic and foreign investors to invest in renewable energy projects, including providing tax breaks to produce renewable energy [84,85]. Investments are the main factor driving the increase in renewable energy consumption in all sectors, including agriculture [86,87].

#### **3. Materials and Methods**

EU countries were deliberately selected for research, as of 31 December 2018. The research period covered the years 2004 to 2018. The sources of materials were the literature on the subject, Eurostat data, and IEA (International Energy Agency) data.

The first stage presents issues related to renewable energy in the EU. The aim was to show the similarities and differences between EU countries. The differentiation between individual countries regarding the declared share of renewable energy in total energy until 2020 was presented. Subsequently, the degree of concentration of renewable energy consumption in the EU and changes in this regard were determined. Gini's associate was used for this purpose. The degree of concentration is measured by the amount of renewable energy consumed in the EU. If these values concern only one country, the coefficient would be 1. If they are spread over more countries, the coefficient becomes lower; the closer to 0, the more even the distribution of the volume of renewable energy consumption among EU countries. The Lorenz curve is a graphical representation of the degree of concentration of the volume of renewable energy consumption in EU countries.

The Gini coefficient is a measure of unevenness (concentration) of distribution of a random variable. When the observations are sorted in ascending order, the coefficient can be represented by the formula [88]:

$$G(y) = \frac{\sum\_{i=1}^{n} (2i - n - 1) \times y\_i}{n^2 \times \overline{y}} \tag{1}$$

where:

*n*—number of observations, *yi*—value of the "*i*-th" observation, *y*—the average value of all observations, i.e., *y* = <sup>1</sup> *n n* ∑ *i*=1 *yi*.

The Lorenz curve determines the degree of concentration of a one-dimensional random variable distribution [89]. With sorted observations *yi*, which are non-negative values <sup>0</sup> <sup>≤</sup> *<sup>y</sup>*<sup>1</sup> <sup>≤</sup> *<sup>y</sup>*<sup>2</sup> ≤··· ≤ *yn*, *<sup>n</sup>* ∑ *i*=1 *yi* > 0, the Lorenz curve is a polyline whose apexes (*xh*, *zh*), for *h* = 0, 1, . . . , *n*, have the following coordinates:

$$\mathbf{x}\_0 = z\_0 = 0, \quad \mathbf{x}\_h = \frac{h}{n'} \quad z\_h = \frac{\sum\_{i=1}^{h} y\_i}{\sum\_{i=1}^{n} y\_i} \tag{2}$$

The Gini coefficient determines the area between the Lorenz curve and the diagonal of a unit square multiplied by 2.

EU countries were deliberately selected for research, as of 31 December 2018. The research period covered the years 2004 to 2018. The sources of materials were the literature on the subject, Eurostat data, and IEA (International Energy Agency) data.

In the second stage of the research, descriptive statistics relating to the share of renewable energy in individual EU countries were presented. This part of the research aimed to obtain information on regularities occurring in individual EU countries and in the entire EU. Statistics analyzed include the average, median, minimal, maximal, standard deviation, coefficient of variation, skewedness, curtosis.

The third stage focused on the use of renewable energy in agriculture. As the Eurostat data do not contain precise data on renewable energy consumption in individual sectors (including agriculture), it was decided to use IEA (International Energy Agency) data. The consumption of renewable energy in the agricultural sector is presented, but it refers only to renewable energy produced in agriculture (primary solid biofuels, biogases and liquid biofuels). Originally, data was collected for all EU countries. After verification, it turned out that in six countries the data was incomplete. In Croatia, Ireland, Malta and Slovenia, the consumption of bioenergy of agricultural origin in agriculture was not recorded but was generated in this sector. In Cyprus and Portugal, the consumption of bioenergy of agricultural origin in agriculture was not recorded in the first years of the period considered. Therefore, it was decided not to include these countries in the analysis. As a result, 22 EU countries were subjected to the study. At this stage of the research, the degree of concentration of bioenergy of agricultural origin consumption in agriculture in individual EU countries was shown. The Gini coefficient was used for this purpose. Graphically, the concentration level is represented by the Lorenz curve. These methods have been described earlier.

In the fourth stage, the dynamics of changes in bioenergy consumption of agricultural origin in agriculture in individual EU countries were determined. As a result, the following trends were observed. Additionally, the research period was divided into three- to fouryear periods. As a result, changes in particular periods were more visible. The dynamics indices with a constant base were used for the research. The constant-based dynamics index has the following formula [90]:

$$i = \frac{y\_n}{y\_0} \text{ or } i = \frac{y\_n}{y\_0} \times 100\% \tag{3}$$

where:

*yn*—the level of the phenomenon in a certain period, *y*0—level of the phenomenon during the reference period.

In the fifth stage, descriptive statistics concerning the share of renewable energy consumption in agriculture (coming only from primary solid biofuels, biogases and liquid biofuels) in the total consumption of renewable energy from this sector were presented. Thanks to this, it is possible to identify regularities occurring in individual countries, as

in the entire EU. Agriculture contributes to renewable energy production but is generally responsible for the low consumption of this energy type, especially produced in this sector.

In the sixth stage of the research, non-parametric tests were used to establish the correlation between the variables. The first is Kendall's tau correlation coefficient. It is based on the difference between the probability that two variables fall in the same order (for the observed data) and the probability that they are different. This coefficient takes values in the range <−1, 1>. Value 1 means full match, value 0 means no match of ordering, and value -1 means complete opposite. The Kendall coefficient indicates not only the strength but also the direction of the relationship. It is a good tool for describing the similarity of the data set orderings. Kendall's tau correlation coefficient is calculated by the formula [91]:

$$\tau = P[(\mathbf{x}\_1 - \mathbf{x}\_2)(y\_1 - y\_2) > 0] - P[(\mathbf{x}\_1 - \mathbf{x}\_2)(y\_1 - y\_2) < 0] \tag{4}$$

The given formula estimates Kendall's tau based on a statistical sample. All possible pairs of the sample observations are combined, and then the pairs are divided into three possible categories:

*P*—compatible pairs, when the compared variables within two observations fluctuate in the same direction, i.e., either in the first observation both are greater than in the second, or both are smaller,

*Q*—incompatible pairs, when the variables change in the opposite direction, i.e., one of them is greater for this observation in the pair, for which the other is smaller,

*T*—related pairs when one of the variables has equal values in both observations.

The Kendall tau estimator is then calculated from the formula:

$$
\pi = \frac{P - Q}{P + Q - T} \tag{5}
$$

Additionally,

$$P + Q + T = \left(\frac{N}{2}\right) = \frac{N(N-1)}{2} \tag{6}$$

where:

*N*—sample size.

The pattern can be represented as:

$$\pi = 2\frac{P - Q}{N(N - 1)}\tag{7}$$

The second non-parametric test is Spearman's rank correlation coefficient. It is used to describe the strength of the correlation of two features. It is used to study the relationship between quantitative traits for a small number of observations. Spearman's rank correlation coefficient is calculated according to the formula [92]:

$$r\_S = 1 - \frac{6\sum\_{i=1}^n d\_i^2}{n(n^2 - 1)}\tag{8}$$

where:

*di*—differences between the ranks of the corresponding features *xi* and feature *yi* (*i* = 1, 2, ... , *n*).

The correlation coefficient takes values in the range −1 ≤ *rs* ≤ +1. A positive sign of the correlation coefficient indicates a positive correlation, while a negative sign indicates a negative correlation. The closer the modulus (absolute value) of the correlation coefficient is to one, the stronger the correlation between the examined variables.

The following methods were used to present the materials: descriptive, tabular and graphic.

#### **4. Results**

In 2019, most of the energy in the world came from crude oil—33.1% (in 2010 it was 34.7%), then coal—27% (29.8%) and natural gas—24% (22.4%), while nuclear energy provided 4.3% (5.2%). Renewable energy accounted for 11.4% (7.8% in 2010) of sources, of which 6.4% (unchanged) was hydropower. Overall, it can be said that changes in the structure of global energy consumption are small, but are in the generally desirable direction, i.e., a decrease in the share of fossil fuels (by 5.5 percentage points) and an increase in renewable sources (by 3.6%) [93,94].

#### *4.1. Renewable Energy in EU Countries*

There was a large variation in the share of energy from renewable sources in the total energy consumption in the EU countries (Figure 1). Countries were using renewable energy to a very large extent (Sweden, Latvia, Finland), but also to a small extent (Malta, Luxembourg, The Netherlands). Each country submitted declarations of achieving a certain share of renewable energy in total energy consumption. Based on the 2018 data, it can be concluded that slightly more than half of the countries will achieve their targets. Natural and economic factors may cause the existing differentiation in goals and the possibility of achieving them.

**Figure 1.** Share of energy from renewable sources in gross final energy consumption in EU countries in 2018.

Then, the degree of concentration of renewable energy consumption in the EU countries was determined. For this purpose, the Gini coefficient was used. This coefficient is a correct and commonly used measure of inequality because it meets all the postulated axioms in this respect. It assumes values in the range from 0 to 1. A result close to 1 means that there is a very high concentration of energy consumption in one country, and close to 0 means that consumption is dispersed across many countries. The number of observations was 28 (all EU countries). The results are presented for the consumption of renewable energy. The Gini coefficient for total renewable energy consumption in 2004, calculated from the sample, was 0.57, and the estimated coefficient for the population was 0.59. This meant quite a high concentration of renewable energy consumption in several EU countries. In the case of repeating the research for 2018, the results were virtually identical (sample coefficient 0.56 and estimated for the population 0.58). Therefore, there have been no significant changes in the distribution of renewable energy consumption in EU countries. The existing differentiation was also presented by means of the Lorenz concentration curve (Figure 2). In 2018, most renewable energy was consumed in Germany, France, Italy, Sweden and Spain. In these five countries, the combined use of renewable energy accounted for 54% of total renewable energy consumption in the EU. In total, the top 10 countries used 79% of the total EU renewable energy consumption. As a rule, most

renewable energy was consumed in economically developed countries and the largest countries in terms of socio-economic potential. Concentration ratios were also calculated for the earlier periods, with a frequency of every three or four years. As a result, the results concern the years 2005 to 2018. Such a combination allows to determine the direction and pace of changes in the concentration of renewable energy consumption. Generally, it can be noticed that the concentration of renewable energy consumption is maintained in several countries (Table 1). One of the reasons may be a fairly stable rate of increase in the consumption of renewable energy in individual countries and the use of technologies that ensure similar energy efficiency.

**Figure 2.** Lorenz concentration curves for renewables and biofuels consumption in the EU countries in 2004 and 2018.

**Table 1.** Gini coefficients for renewables and biofuels consumption in the EU countries in 2004 to 2018.


The share of energy from renewable energy sources changed from 2004 to 2019 (Table 2). The highest average share of energy from renewable energy sources were in Sweden (48.45%), Latvia (34.99%) and Finland (34.90%). It is worth mentioning that the average in Poland was 9.74% and it increased 21.22% (Table 1). The lowest share of energy from renewable energy sources was found in the analyzed period in Malta (3.14%), Luxemburg (3.85%) and The Netherlands (4.73%). The average share of energy from renewable sources for the EU 28 was 13.93%. It is worth mentioning that Iceland (70.55%) and Norway (64.66%) achieved a much higher share than the EU.

We have also analyzed the minimal share of energy from renewable sources. As we can see from Table 1, the lowest minimal share of energy from renewable sources was in 2004 in Malta (0.10%), Luxemburg (0.90%), and United Kingdom (1.10%). The highest minimal share of energy from renewable sources in 2004 was in Latvia (29.62%), Finland (28.81%), and Sweden (38.68%). These countries also had the highest maximal share of energy from renewable sources in 2019, respectively (40.98%, 43.08%, and 56.39%).

The coefficient of variation informs about the changes that were in the analyzed variable. The biggest changes were observed in Malta (99.00%), United Kingdom (67.32%), Luxemburg (56.98%), and Ireland (45.68%). The smallest changes were found in Slovenia (6.75%), Croatia (9.71%), and Latvia (10.78%).

Skewedness was positive in the following countries: Denmark, Ireland, Greece, France, Cyprus, Latvia, Lithuania, Luxemburg, Malta, The Netherlands, Slovakia, Finland, and

United Kingdom. It means that the tail on the right side of the distribution is longer than the left side. Other countries reached negative skewedness.

Kurtosis is also an asymmetry measure. The data proved that the kurtosis reached a positive value for 2004 to 2019 only in Luxemburg and Slovakia. The vast majority of the EU countries achieved a negative value, indicating that the measure was different in 2004 to 2019 in relation to the mean.

**Table 2.** Descriptive statistics of share of energy from renewable sources (%) in the EU in 2004 to 2019. Red font indicates the lowest results. Bold font indicates the highest scores.


#### *4.2. Consumption of Bioenergy of Agricultural Origin in Agriculture*

Agriculture is one of the many sectors that can benefit from renewable energy. The article presents energy consumption in agriculture, but coming from primary solid biofuels, biogases, and liquid biofuels, i.e., agricultural energy. The Gini coefficient was used to determine the degree of concentration of such energy consumption in the agricultural sector. In 2004, the Gini coefficient calculated from the sample was 0.32, and the estimated coefficient for the population was 0.33. This meant a relatively low concentration of bioenergy of agricultural origin consumption in agriculture in several EU countries. In the case of repeating the research for 2018, the results were identical. Therefore, there have been no significant changes in the distribution of bioenergy of agricultural origin consumption in agriculture in the EU countries. The existing differentiation was also presented by means of the Lorenz concentration curve (Figure 3). In 2018, most bioenergy of agricultural origin was used in agriculture in Germany, Poland, France, Finland and The Netherlands. In these five countries, combined use of bioenergy of agricultural origin in agriculture accounted for 65% of total renewable energy consumption in the EU agricultural sector. The top 10 countries used 90% of total bioenergy of agricultural origin consumption in agriculture in the EU. As a rule, most renewable energy in agriculture was used in economically developed countries and countries with large agricultural production and those developing energy from non-renewable sources. The latter factor may even be decisive. Concentration coefficients were also calculated for the earlier periods, with a

frequency of every three or four years. As a result, the results relate to the years 2004 to 2018. Such a statement allows determining the direction and pace of changes in the concentration of bioenergy of agricultural origin consumption in agriculture. Generally, it can be noticed that the concentration of bioenergy of agricultural origin consumption in agriculture is maintained in a dozen or so countries, of which usually two to three countries consumed this energy the most (Table 3). Individual countries changed at the positions of leaders. In 2004, Sweden consumed the most bioenergy of agricultural origin in agriculture; in 2008 and 2011, it was Poland; in 2014 and 2018, it was Germany. Despite these changes between countries, the level of concentration has remained unchanged. One of the reasons may be a certain stabilization in agricultural production and a fairly stable pace of growth in renewable energy consumption in individual countries. This is because countries use technology that provides similar energy efficiency.

**Figure 3.** Lorenz concentration curves for bioenergy of agricultural origin consumption in agriculture in the EU countries in 2018.

**Table 3.** Gini coefficients for bioenergy of agricultural origin consumption in agriculture in the EU countries in 2004 to 2018.


The use of bioenergy of agricultural origin in agriculture varied across countries. The dynamics of changes was also different (Table 4). The use of bioenergy of agricultural origin in agriculture was the fastest in The Netherlands, Italy and Belgium. In the years 2004 to 2018, in these countries, there was an increase of several dozen times. Of these, only The Netherlands has achieved a high volume of bioenergy of agricultural origin consumption in agriculture. In Italy and Belgium, the starting level in 2004 was low, so despite the high dynamics, the level in 2018 was still relatively low. Only in Sweden has the consumption of bioenergy of agricultural origin in agriculture decreased. Despite this, the country was characterized by a high volume of bioenergy of agricultural origin consumption in agriculture. High growth dynamics were achieved among the countries with the highest volume of bioenergy of agricultural origin consumption in agriculture, i.e., in Germany (increase by 427%) and Poland (by 187%). In most countries, the highest increases in the consumption of bioenergy of agricultural origin in agriculture were recorded in 2004 to 2008, and the lowest in 2014 to 2018. It can therefore be concluded that bioenergy of agricultural origin is introduced more and more slowly in agriculture. In some countries, there was stagnation or a decrease in the consumption of this energy.


**Table 4.** Dynamics indicators for the consumption of bioenergy of agricultural origin in agriculture in the EU countries in 2004 to 2018.

Individual EU countries differed in terms of the level of bioenergy of agricultural origin consumption in agriculture. Indicators can also be used to determine the importance of bioenergy of agricultural origin in agriculture. One of them is the share of bioenergy of agricultural origin consumption in agriculture (coming only from primary solid biofuels, biogases and liquid biofuels) in the total consumption of renewable energy from this sector. Particular descriptive statistics allowed for the identification of regularities occurring in individual countries and the entire EU (Table 5). In 2004 to 2018, the highest average share of bioenergy of agricultural origin in agriculture was recorded in The Netherlands and Poland (over 10%). In turn, it was the lowest in Italy, Romania and Bulgaria. In most EU countries, the median was most often close to the arithmetic mean value. The lowest minimum share of bioenergy of agricultural origin consumption in agriculture in the total consumption of renewable energy from agriculture was in Italy (0.01%) and Romania (0.05%), and the highest in Luxembourg (4.75%) and Poland (4.52%). In the case of the maximum values, the lowest share was in Italy (0.52%) and Romania (1.16%), and the highest in The Netherlands (20.56%) and the United Kingdom (16.72%). The smallest difference between the maximum and minimum value was in the case of Italy (0.52 percentage points) and Romania (1.11), and the largest in The Netherlands (20.12) and the United Kingdom (13.35).


**Table 5.** Descriptive statistics of share of energy consumption from bioenergy of agricultural origin in agriculture (%) in the EU in 2004 to 2019. Red font indicates the lowest results. Bold font indicates the highest scores.

> The variability of the examined index of the share of bioenergy of agricultural origin in agriculture was also determined. The greatest stabilization was in Austria (the coefficient of variation was 9.47%) and Finland (11.85%), and the greatest in Italy (133.36%) and Romania (112.68%). In most EU countries, volatility was very high. For the entire EU, the coefficient of variation was around 20%.

> Skewness was positive in most EU countries, which means that the results were higher than the average for most of the years studied. Particularly high results were achieved in Romania and Bulgaria. On the other hand, most of the below-average results were achieved in Poland and France. Kurtosis is a measure of how results are concentrated around the mean. Results were positive in about half of the countries and negative in another half. A lot of results concentrated around the average were recorded in Poland and Hungary, and the lowest in Sweden and the Czech Republic. In general, it can be stated that there was a large variation between EU countries in the share of bioenergy of agricultural origin consumption in agriculture (coming only from primary solid biofuels, biogases and liquid biofuels) in the total consumption of renewable energy from this sector.

> To establish the relationship between the amount of energy consumption from bioenergy of agricultural origin in agriculture in the EU countries and the basic parameters of the economy, energy and agriculture, Kendall's tau correlation coefficient and Spearman's rank correlation coefficient were calculated (Table 6). *p* = 0.05 was adopted as the border value of the significance level. Significant results are marked in bold in the table. Correlation coefficients were calculated for all EU countries (28 countries) for the entire 2004 to 2018 period. The study tried to check the correlation, which does not indicate that a given factor affects another, but a strong or weak relationship between them.


**Table 6.** Kendall's tau correlation coefficients and Spearman's rank correlation coefficients between the volume of bioenergy of agricultural origin consumption in agriculture in the EU countries and the parameters of the economy, energy and agriculture. Bold font indicates the highest scores.

In the case of Kendall's tau correlation, significant positive relations were found for almost all parameters with the amount of bioenergy of agricultural origin consumption in agriculture in the EU. The strength of the relationship was very great for the economic parameters. These relationships were very strong for both the global performance and percapita performance parameters. Energy-related parameters were also strongly correlated with the consumption of bioenergy of agricultural origin in agriculture. The only exception was the parameter on total energy consumption in agriculture. The dependencies were varied, as a positive correlation was found in relation with the total consumption of renewable energy, and a negative correlation in the total energy consumption. This means that changes in the consumption of bioenergy of agricultural origin in agriculture follow the same direction as changes in the renewable energy consumption in the economy. There is an overall reduction in energy consumption in the EU; therefore, there was a negative correlation for this parameter. The parameters related to agriculture had less correlation with bioenergy consumption of agricultural origin in the agricultural sector. A strong positive relationship was observed for gross value added of agriculture, forestry and fishing, and a very strong positive one for raw cows' milk delivered to dairies. Both parameters showed an upward trend. Negative strong and average relations were found for the total agricultural area and agricultural area of grain, respectively. Overall, these areas slightly decreased, while the consumption of bioenergy of agricultural origin in agriculture increased. The presented correlation results indicate that there were very strong relationships between the volume of bioenergy of agricultural origin consumption in agriculture and the economic potential and the level of economic development. The general situation in the economy was more decisive. When favorable, it also fueled agriculture and favored more work. In turn, the economic crisis also affected agriculture and led to a reduction in production. In land-related parameters, these relationships were negative, because land resources do not increase but even decrease. In turn, the consumption of bioenergy of agricultural origin in agriculture grew, including the more and more common use of this type of energy, cheaper technologies, and the promotion of renewable energy. In animal production, milk production increased, which was positively correlated with bioenergy consumption of agricultural origin in agriculture. It should also be noted that there were also various correlations with different parameters of energy consumption in the economy. It all depended on the existing trend. Total energy consumption was falling, so it was negatively correlated with bioenergy of agricultural origin in agriculture. In turn, the consumption of renewable energy in the economy increased, which meant a positive correlation.

The analysis carried out with the use of Spearman's rank correlation coefficients gave very similar results. However, the strength of the relation was much greater. Both tests confirm the close relation between bioenergy of agricultural origin consumption in agriculture and economic and energy-related parameters, and a smaller one with agricultural parameters.

#### **5. Discussion**

Kazar and Kazar [95] stated that, in the long term, economic development will lead to the production of renewable energy. In a short time, there is a two-way causal link between renewable energy production and economic development. The study covered 154 countries between 1980 and 2010. In turn, Apergis and Payne [96] found that this relationship is bidirectional in both the short and long term. The study was conducted for a panel of 20 OECD countries over the period of 1985 to 2005. Sadorsky [97] also found such relationships based on a study of 18 economies of developing countries. Over the long term, a 1% increase in real per capita income has increased the per capita renewable energy consumption by around 3.5% in these economies. Similar two-way relationships were found in the studies by Pao and Fu [98] examining Brazil, Lin and Moubarak [99] studying China, Shahbaz et al. [100] examining Pakistan, and Khoshnevis Yazdi [101] studying Iran. The one-way causality between renewable energy and economic growth was stated by Leitão [102] in the Portuguese economy. This study was conducted for the period of 1970 to 2010, using time series (OLS, GMM, unit root test, VEC model, and Granger causality). The Granger causality reports a unidirectional causality between renewable energy and economic growth. Bhattacharya et al. [103] carried out studies on 38 countries with the highest renewable energy consumption. Renewable energy consumption has had a significant positive impact on economic performance in most of the countries surveyed. Similar results were obtained in the study by Saidi and Omri [104] on the example of 15 countries with the highest consumption of renewable energy. The fully modified ordinary least square (FMOLS) and the vector error correction model (VECM) techniques were used. A bidirectional causality between economic growth and renewable energy in the short- and long-run for both models was found. Menegaki [105] performed research on a sample of 27 European countries. The empirical results do not confirm a causal relationship between renewable energy consumption and GDP. However, this study covered the period of 1997 to 2007, before the targets for 20% of renewable energy in total energy were set. A study from 2004 to 2017 by Busu [106] confirmed the relationship between renewable energy and economic growth in 28 EU countries. Biomass production has had the most significant impact on economic growth of all renewable energy sources. According to the author, in the analyzed period, an increase in the basic production of biomass by 1% would affect the economic growth by 0.15%. Similar results were obtained by Armeanu et al. [107] for the years 2003 to 2014. The 1% increase in primary production of solid biofuels increased GDP per capita by 0.16%. The vast majority of studies have found a two-way relationship between renewable energy production and economic growth. In particular, such results were achieved when the share of renewable energy in the studied countries was already significant. In addition, it must also be remembered that almost all of its production is spent on domestic consumption in the case of renewable energy. There was little trade in this type of energy.

Similarly, as in other sectors, agriculture is also closely related to energy. The increase in agricultural production is positively correlated with energy consumption. In most EU countries, the technical and technological modernization of agriculture directly affects the lower energy consumption of production [108–112]. In the case of agriculture, the studies by Alola and Alola [113] found a one-sided relationship between the use of agricultural land and the consumption of renewable energy. There was no feedback. The research results concerned 16 countries of the Mediterranean coast in the years 1995 to 2014. Ben Jebli and Ben Youssef [114] found in their research a long-term two-way causal relationship between renewable energy consumption and agricultural value added (AVA). The study concerned Tunisia in the years 1980 to 2011. The research was repeated in Morocco [115] and five North African countries [116]. In this case, too, a two-way relationship in consumption between renewable energy consumption and agricultural value added (AVA) was found. Khan et al. [117] obtained similar results on in their study of Pakistan. They showed a multilateral relationship between AVA, renewable energy consumption, and carbon dioxide emissions. The research covered the years 1981 to 2015. According to these authors, Rehman et al. [118] and Ali et al. [119], the government in Pakistan should support the growth of the AVA, because it will contribute to a greater use of renewable energy and, consequently, lower emissions of pollutants into the environment. Additionally, it is necessary to introduce modern technologies [120]. The authors suggest that increasing international economic exchange will allow the agricultural sector to develop and benefit from the transfer of renewable energy technologies. Aydo ˘gan and Vardar [121] argue that increasing the share of renewable energy may increase production in the agricultural sector. The balanced panel data set of E7 countries (Emerging Seven—Brazil, China, India, Indonesia, Mexico, Russia and Turkey) over the period 1990 to 2014 was used. Liu et al. [122] suggest that the development of sustainable agriculture can promote renewable energy. The research in four selected countries of the Association of Southeast Asian Nations (ASEAN-4: Indonesia, Malaysia, the Philippines, and Thailand) in 1970 to 2013 was made.

Jebli and Youssef [123], using data from Argentina in 1980 to 2013, found that agriculture and renewable energy production are substituting and competing for land use. Such substitution and competition should be limited by encouraging R&D in the production of second- or third-generation biofuels and new technologies for renewable energy, or the increase in agricultural productivity per unit area. Quite a controversial statement was made by Al-Mulali et al. [124]; according to these authors, the production of renewable energy increases the inefficiency of land and water use. The research was performed in 58 developed and developing countries in the years 1980 to 2009. Destek and Sinha [125] thought the opposite. Consuming renewable energy reduces environmental impacts. The authors studied OECD countries in 1980 to 2014. Similar results were obtained in the study by Destek et al. [126], which concerned the EU countries in 1980 to 2013. In the studies of Solarin et al. [127], it was noted that there are differences between types of renewable energy. To reduce carbon dioxide emissions, there is a need to replace fossil fuels with other renewable energy sources (e.g., hydropower) rather than energy from biomass. The research covered 80 developed and developing countries in the period 1980 to 2010. In a study by Wang [128] on Brazil, Russia, India, China and South Africa (BRICS countries), a high impact of biomass in reducing environmental pollution was found. Energy from biomass was treated as a clean energy source. The research covered the years 1992 to 2013. Based on the presented review, it can be concluded that the perception of renewable energy also depended on the period covered by the research. More recent data show that renewable energy has a significant environmental impact. Therefore, it is purposeful to promote this energy on farms instead of conventional energy. An example is the largest biogas market in the world. The development of the German biogas sector has mainly been triggered and driven by consecutive versions of the Renewable Energy Act (REA) and accompanying regulations [129]. It was similar in the USA, where new tax incentives were introduced [130]. Piwowar [131] stated that in Poland, institutional support is necessary and increases the awareness of farmers. The low propensity of farmers to use renewable energy technologies was also found in other EU countries. An example was, among others, Ireland [132]. Energy self-subsistence of agriculture is particularly desirable in European agriculture, where the individual farm sizes are average [133].

#### **6. Conclusions**

Maintaining the natural environment in a proper condition requires an energy transformation. There is a need for an increased use of renewable energy sources. The EU has adopted targets for the share of these energy sources in total energy consumption. Each country has different limits that it has pledged to meet by 2020. The declarations resulted from the current state of development of renewable energy in a given country

and technical possibilities and the level of economic growth. In the years 2004 to 2019, the production of renewable energy in the EU was relatively high and increased in each country at a similar pace. However, the starting potentials were different, and there are still significant differences between countries.

There has been a relatively high concentration of renewable energy consumption in several countries. The level of concentration has not changed, but there have been some changes in the order of countries. The reason is the development of renewable energy production in all countries and the use of similar technology. There was some stabilization in countries with a high share of renewable energy in total energy consumption. The dynamics of changes were slight, but these countries already had an established position. Countries with less renewable energy increased their consumption volumes very quickly but started far below the leaders. Therefore, very high dynamics of changes can be misleading. As a result, there were still disproportions.

The use of bioenergy of agricultural origin in agriculture, but produced in this sector, was quite dispersed across many countries. The level of concentration has not changed. There have been some changes in the positions of individual countries. Thus, the first research hypothesis was not confirmed. As a rule, most renewable energies was used in agriculture in economically developed countries, but with large agricultural production, such as Germany, Poland, France, Finland, and Spain. The factor related to the development of obtaining energy from non-renewable sources can be a decisive factor. One of the reasons was changes in the level of concentration of non-renewable energy consumption in agriculture. It may stabilize in agricultural production and a relatively stable rate of increase in renewable energy consumption in individual countries. The agricultural sector changes relatively slowly compared to other sectors of the economy. For faster changes, large investment outlays are necessary, but also greater project support.

There were very large differences between EU countries in terms of basic statistics on the share of bioenergy of agricultural origin in agriculture but coming from energy produced in this sector. The average percentage in a few countries was above 10%, and in a few countries, it was below 1%. In addition, in the years 2004 to 2018, there were large differences between the maximum and minimum share of bioenergy of agricultural origin in agriculture in countries such as The Netherlands and the United Kingdom, and small ones in Italy and Romania. Interestingly, Italy and Romania were the countries with the most significant variability in the share of bioenergy of agricultural origin in agriculture. The reason was the very low shares in these countries, not exceeding 1.2%. There were also differences between countries in terms of measures of asymmetry and concentration. Overall, there was a very wide variation between EU countries in the share of bioenergy of agricultural origin in agriculture. It was more significant than for the entire sector of the economy. This may indicate that the agricultural sector is less innovative and less willing to introduce changes than the whole economy.

A correlation was found between the consumption of bioenergy of agricultural origin in agriculture for the entire EU and individual economic parameters in the field of energy and agriculture. The strength of the relationship varied. Only in the case of total energy consumption in agriculture was there no relationship. There was a high positive correlation in the relationship between bioenergy of agricultural origin consumption in agriculture and economic parameters. This means that economic development contributes to the greater use of bioenergy of agricultural origin in agriculture. In the case of energy-related parameters, the relationships were not clear. Total energy consumption, i.e., a parameter that tended to decrease, was negatively correlated with bioenergy consumption of agricultural origin in agriculture. The parameter of total renewables and biofuels consumption, which showed an upward trend, was positively correlated. There was also a differentiation in the parameters related to agriculture. The dependencies here were similar to those for energy. A positive correlation was found for gross value added of agriculture, forestry, and fishing, as well as for raw cows' milk delivered to dairies. The production parameters of agriculture increased. On the other hand, the negative correlation was with parameters with a downward trend, such as the area of agricultural crops and area of grain sowing. The second hypothesis was confirmed, according to which the consumption of bioenergy of agricultural origin in agriculture was closely related to the parameters of agricultural production. However, it should be added that the relations were positive or negative depending on the trend in the case of a given parameter related to agriculture.

Overall, it must be stated that the consumption of bioenergy of agricultural origin in agriculture from this sector was at a low level and was growing very slowly. This is due to the low propensity to innovate in the agricultural sector. It can also be stated that the potential of agriculture related to the production of renewable energy for energy consumption on a farm is not used. There is much to be carried out in this regard. Apart from the appropriate information campaign, it is necessary to financially support initiatives contributing to the self-supply of farms with energy.

The limitation of the research is the poor availability of data. Information is aggregated. It is difficult to obtain data on the type of renewable energy used in agriculture that is produced in agriculture. For example, solar energy can be used in a farmer's household and on a farm. There is a need to perform micro-level research concerning farms in one locality or individual farms. The results may differ from one EU country to another.

**Author Contributions:** Conceptualization, T.R.; methodology, T.R.; software, T.R.; validation, T.R.; formal analysis, T.R., P.G.; investigation, T.R.; resources, T.R.; data curation, T.R.; writing—original draft, T.R., M.R., P.B., A.B.-B., B.G., P.G., A.S.; writing— review and editing, T.R., M.R., P.B., A.B.-B., B.G., P.G., A.S.; supervision, T.R.; funding acquisition, T.R., M.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Jakub Jasi ´nski 1,\*, Mariusz Kozakiewicz <sup>2</sup> and Maciej Sołtysik <sup>3</sup>**


**Abstract:** The European Green Deal aims to make Europe the world's first climate-neutral continent by 2050 by shifting to a clean circular economy, combating biodiversity loss and reducing pollution levels. In Poland, whose economy invariably remains one of the most dependent on coal consumption in Europe, institutional responses to the above EU objectives have taken the shape of energy cooperatives aimed at filling the gaps in the development of the civic dimension of energy on a local scale and the use of potential renewable energy sources in rural areas, including in relation to the agricultural sector. This article is a continuation of the authors' previous research work, which has so far focused on the analysis of the development of profitability of Polish institutions that fit into the European idea of a "local energy community", which includes energy cooperatives. In this research paper, they present the results of subsequent research work and analyses performed on the basis of it which, on the one hand, complement the previously developed optimization model with variables concerning actual energy storage and, on the other hand, analyze the profitability of the operation of energy cooperatives in the conditions of the "capacity market". The latter was actually introduced in Poland at the beginning of 2021. The research took account of the characteristics of energy producers and consumers in rural areas of Poland, the legally defined rules for the operation of the capacity market and the institutional conditions for the operation of energy cooperatives that can use the potential of energy storage. A dedicated mathematical model in mixed integer programming technology was used, enriched with respect to previous research, making it possible to optimize the operation of energy cooperative with the use of actual energy storage (batteries). Conclusions from the research and simulation show that the installation of energy storage only partially minimizes the volume of energy drawn from the grid in periods when fees related to the capacity market are in force (which should be avoided due to higher costs for consumers). The analysis also indicates that a key challenge is the proper parameterization of energy storage.

**Keywords:** energy cooperatives; capacity market; energy storage; rural areas; mixed integer programming

#### **1. Introduction**

Decreasing amounts of raw material and constantly increasing pro-environmental pressure make it necessary to look for solutions to increase the efficiency of the use of resources and the optimization of their use [1]. Based on social relationships, the global sharing economy trend is changing fundamental organizational and distribution models and is built on a network of integrated individuals and communities [2]. This phenomenon, which is based on the human tendency to cooperate [3], to share and exchange resources, is beginning to encompass more and more spheres of social life, including the electricity market sector [4,5]. The EU policy imposes a direction for the reorganization of the

**Citation:** Jasi ´nski, J.; Kozakiewicz, M.; Sołtysik, M. The Effectiveness of Energy Cooperatives Operating on the Capacity Market. *Energies* **2021**, *14*, 3226. https://doi.org/10.3390/ en14113226

Academic Editor: Talal Yusaf

Received: 26 April 2021 Accepted: 27 May 2021 Published: 31 May 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

generating sector by supporting the creation of self-balancing energy areas (regions and communities) [6,7], where energy generation is based on renewable sources (Clean Energy Package (CEP)) [8].

Building local energy independence manifests itself in the creation of cooperatives, enabling benefits to be drawn by cooperating demand and supply side entities. In the EU, creating energy self-sufficiency at the local level is possible on the basis of institutions called energy communities (EC) [9,10]. Their structure and operating model correspond with the guidelines set out in (i) the REDII directive [11], where the focus has been on the *Renewable Energy Community* [12], and it is also a result of (ii) the "electricity market directive" [13], where the *Citizens Energy Community (CEC*) is promoted. Creating opportunities for building local energy communities [14] can be of great importance, especially in rural areas. This is where the greatest potential exists in terms of the use of renewable energy sources (including biomass and biogas), as well as potential waste.

The Polish responses [15] to the development of local energy communities promoted in the EU are characterized by energy clusters [16] and the energy cooperatives analyzed in this article, which the creators of this form of energy cooperation intend to be established in rural areas. The object and the scope of activity of energy cooperatives, as well as the conditions affecting their creation and operating profitability, have already been assessed and described by the authors of this research paper [17]. The considerations and analyses carried out earlier focused on illustrating the benefits seen from the perspective of integrating the supply side (producers) and the demand side (consumers) into the structure of a cooperative. However, they did not correspond with the change in energy market activity in Poland from an energy-only market to a capacity market [18]. In Poland, capacity-market mechanisms have been implemented since January 2021, which significantly affect the operating models and market strategies of both power suppliers and consumers, who are the payers of capacity fees [19]. From this perspective, it is reasonable to extend the analyses carried out so far to energy cooperatives, with elements related to their operation in the energy market and the capacity market [20].

The creation of the capacity market in 2018 was one of the largest changes in the Polish power sector in recent years [21]. From 2021 onwards, those in Poland will pay not only for the electricity generated but also for the available capacity of the power system [22]. This means that power plants will be paid both for electricity production and standby capacity, i.e., full availability [23]. On 30 November 2020, Poland's Energy Regulatory Office (URE) announced the electricity rates for consumers [24] that Poles would have seen on their bills from January 2021 [25]. A new item on the bill for electricity supplies is the capacity fee which, for recipients other than households, will be PLN 76.20/MWh (EUR 1 is around PLN 4.5) [26]. The capacity fee provides financing for the capacity market, i.e., mainly for maintaining capacity in readiness and for modernization and construction of new conventional power plants. This fee will depend on energy consumption between 7 am and 10 pm on weekdays but, given that these are standard working hours, the vast majority of energy consumption will be covered by it [27].

The aim of this paper is to present the results of the research concerning the assessment of the actual impact of energy storage (not only based on the virtual network deposit) on the operating efficiency of energy cooperatives, the increase in the degree of energy independence in the conditions of the capacity market and the minimization of energy consumption during the capacity-fee hours. Thus, an attempt will be made to answer the question: with what production, storage and consumer structure and with what number and configuration of cooperative members does a form of self-organization such as an energy cooperative have a chance to develop and improve its operating efficiency? The models developed by the authors so far [17] will be supplemented by the key element for these considerations—batteries (energy stores) [28]—which may determine not only the character and structure of emerging cooperatives but also, in some cases, become a factor determining their profitability [29,30]. The research objectives set by the authors are the analysis and assessment of: (i) whether, through an appropriate choice of generation sources and an energy storage facility, it is possible to avoid consuming energy from the grid (outside the virtual network deposit) [17,31], which makes it possible to avoid capacity fees; (ii) whether it is worth installing high-capacity energy storage, or whether a measurable effect of improving the optimization target can already be achieved by storage with a lower capacity and power; (iii) to what extent it is possible to estimate the volumetric savings in consuming energy from the virtual network deposit through the use of an actual energy store; and (iv) whether it is reasonable to build an actual store for each cooperative and, if not, which cooperatives it would be reasonable for.

All models of cooperative proposed and studied in this paper, as well as the data on production and energy consumption on farms that are potential members of a cooperative, are anonymized real data obtained from rural areas of Poland. In addition, real data and parameters of energy stores were used in the analyses, so that the conclusions and recommendations from the analyses take on a real and practical dimension.

The paper is structured as follows. The second section provides the main characteristics of the capacity-market model in Poland. The institutional description and legally defined rules for the operation of energy cooperatives can be found in the research team's previous paper [17]; they have therefore not been reproduced in detail in this article. The next section describes the assumptions for the selection of the research sample, taking into account the formal conditions for the establishment of energy cooperatives and the specific character of farms in rural areas in Poland, the input data and their selection, as well as the optimization method, developed in relation to previous analyses, which was used in the research process. The results of the research are then presented and discussed in the subsequent section of the article. In conclusion, it was possible to summarize all the completed work and research, and potential areas of further research interest to the authors have been indicated.

#### **2. Background—The Capacity Market in Poland**

The diagnosis of a permanent and continuously growing power shortage, seen both in the short- and long-term scenario [32], was clearly stated by the Polish Transmission System Operator (TSO) in 2014–2015 [33]. In the context of the problems identified at that time, it became a strategic objective to plan changes in the functional model of the energy market in Poland in such a way as to prioritize the security of electricity supply in the Polish Power System (PPS) [34]. The need for changes was an effect of the situation in the regulatory and market environment, which resulted in the permanent exclusion of some centrally dispatched generating units that are critical from a PPS point of view [35]. Maintaining the energy market as an energy-only market would clearly lead to energy supply disruptions and, consequently, to high costs for the economy for not supplying energy [36]. Two main phenomena that influenced the implementation of capacity mechanisms were diagnosed:


The problem of missing money was due to the fact that the revenues from the units critical to the safety of system operations did not cover their operating and capital costs. An analysis of the short-run marginal cost (SRMC) amounting to about PLN 155–160/MWh (for 2015) for a typical generation system in Poland, i.e., a 200 MW coal-fired power unit, and the average level of wholesale prices on the energy exchange (TGE S.A.) [37], showed that these values were at similar levels. This resulted in the inability to cover the fixed operating costs of the generating unit and a lack of investment impulses for modernization of the existing energy sources and construction of new ones. In 2015, the average operating time of a 200 MW coal-fired power unit was only 3817 h/year (43%).

The phenomena of the low wholesale price and lower volume of energy sold resulted from the conditions of the energy-only market. This model means that the operators of generating units are remunerated based on the production of electricity, which is evaluated on the wholesale market. The valuation of energy is not affected by the type of technology by which it was produced. Low wholesale prices were a consequence of:


The above factors caused the displacement of coal-fired units, the limitation of their operating duration and, consequently, the inability to fully cover the costs of operation and energy production. The forecast of a long-term money-shortage problem clearly indicated a lack of investment incentives over the long term, resulting in a shortfall in capacity.

With the introduction of the capacity market, the "capacity obligation" service was implemented [38]. The generating units covered by this are required to be ready to supply power and to deliver it to the system during a period of threatened shortage, with adequate remuneration. The capacity market is closely related to the development of demand-side response (DSR) services, which consist in the temporary reduction in electricity consumption by consumers or the postponement of its consumption (demand-side management) at the request of the TSO in exchange for remuneration [39].

The need to generate units to remain on standby and supply power to the system during an emergency is related to a cost allocated to all electricity consumers. For households, the cost is of a lump-sum nature and is dependent on the average annual level of energy demand. The rates for this group of consumers range from PLN 1.87/month for consumers with a consumption up to 0.5 MWh/year to PLN 10.46/month for consumers with a consumption above 2.8 MWh/year. Other types of consumers are charged a single rate of PLN 76.20/MWh, calculated for energy consumed on working days between 7 am. and 10 pm. It is worth noting that the capacity fee accounts for approximately 10–15% of the total cost of energy, calculated together with the distribution service [24].

The capacity fee is a component of the distribution fee, and it is therefore related to the energy consumed directly from the grid. In order to reduce the capacity fee, energy consumption from the grid should therefore be reduced. The answer is to find ways to reduce energy consumption or to generate, self-consume and store energy from renewable sources.

#### **3. Materials and Methods (Optimization Model)**

#### *3.1. Assumptions for the Creation of a Sample of Energy Cooperatives for Simulation Purposes*

An assessment of the impact of capacity-market implementation on the level of consumers' costs, on consumers' behavior and on the rationality of building local energy communities required simulations and hypothesis-testing related to simulation scenarios by mapping actual energy cooperatives. For this purpose, real data on electricity production and energy demand in rural areas in Poland was used and five types of energy cooperative were created for simulation purposes. An additional requirement was to represent the diversity of: (i) the locational nature, (ii) the level (scale) of electricity demand, (iii) the nature of economic activity of the cooperative participants, (iv) the electricity consumption profile of each member of the cooperative, (v) the generation potential among the members of the cooperative, (vi) the level of voltage supply from the members of the cooperative, (vii) population size, and (viii) the energy storage capacity.

The selection of members of energy cooperatives took account of locational constraints, i.e., the allocation of members in up to three neighboring rural or rural–urban municipalities. The criteria for the selection of the generation structure by the optimizer took account of at least 70% of the energy demand within the annual billing period, which depended on different types of generation sources and different storage capacities.

The unfavorable hydrological conditions in Poland significantly affect the possibility of using hydro-power for electricity production. For the analyses, it was assumed that a maximum of one hydro-power plant may operate within an energy cooperative, and that there is at least one watercourse that could be adapted for energy generation purposes in the areas of the municipalities where the energy cooperatives were simulated. A practical assumption was adopted, stating that a small hydro-power plant is characterized by low capacity at the level of between several dozen and several hundred kW. The simulation therefore took account of the capacity limits of a single source from 0 to 500 kW, with increments of 50 kW. Discreet increments make the simulation realistic because a source with any continuous capacity cannot be installed.

In Poland, the development of prosumer sources based practically 100% on photovoltaic sources is ongoing and still accelerating. The construction of PV sources is currently the most popular and fastest growing method to achieve energy self-sufficiency in Poland [40]. According to the data from the Ministry of Development, Labor and Technology, at the end of December 2020 [41], there were more than 457,000 micro-systems in Poland (an increase of 28.1% compared to the end of Q3 2020, and as much as 196% more compared to the end of 2019) [42] with a total capacity of about 3006 MW [43]. The dynamics and trends of micro-system growth are influenced by numerous aid programs [44]. In view of the above, for the simulations, it was assumed that at least 25% of energy production of the members of the cooperatives is from solar energy. In addition, capacity limits for an individual PVPP farm from 0 to 1000 kW with increments of 50 kW were adopted, which has a practical justification, since the capacity limit for a micro-system according to the Polish law is 50 kW.

Energy cooperatives can be established in rural and rural–urban areas, i.e., in sparsely urbanized areas [45]. These factors support the construction of low-mast wind sources with low and medium capacity. The efficiency of wind generation is about twice as high for Polish wind conditions as for photovoltaic sources, which makes this type of generation attractive in terms of efficiency and cost [46]. For the analysis and simulation, the ability of cooperative participants to establish sources with a capacity from 0 to 1000 kW with increments of 250 kW was assumed.

Taking account of the location criterion when establishing the cooperative was also intended to take advantage of the agricultural character and potential of the regions, particularly in the context of the stability of the generation profile based on biomass and biogas sources. For the simulation, the capacity limitations of these sources were assumed to be from 0 to 600 kW, with increments of 200 kW. The presence of generation sources of both stochastic (PVPP, wind) and stable (biomass, biogas) generation in energy cooperatives will result in a flattening of the profile and a reduction in generation differences between seasons of the year or times of day.

Additionally, an assumption was adopted indicating that, in the selection of generation sources for the optimal balance of demand in the cooperative, one member has at most two energy generation sources, which does not exclude a situation where not all members have them and are thus energy producers.

The discount nature of the operation of the energy cooperative and its members means that the loss of some energy on its introduction into the distributor's network and its subsequent consumption should be balanced by a slight increase in the installed capacity of the source. For the simulation, it was assumed that the total annual energy production of each member of the cooperative could not exceed 120% of the annual energy demand. This assumption ensures that each member of the cooperative is fully balanced at an individual level and the surplus that occurs further allows the development of self-sufficiency at an aggregated cooperative level. As energy prosumers, cooperatives benefit from a discount model that allows them to manage temporary energy surpluses and shortages. Improving cooperation with the DSO [30] and the efficiency of this mechanism, as well as and increasing real-time energy self-consumption, are further enabled by real energy storage. The addition of real energy storage (batteries) to the model is one of the key elements of the study described in this paper.

#### *3.2. Characteristics of Energy Cooperatives and Energy Storage Used in the Study*

The sample of energy cooperatives used in the study was constructed using actual measurement data and customer and generation profiles for each type of renewable energy source. The purpose of selecting the participants of the cooperative was to reflect:

• The location character—the simulation was made for participants in two southern voivodeships (administrative divisions) of Poland, Małopolskie and Sl ˛ ´ askie, and the

selection took account of different locations of municipalities within the voivodeships. The selection of two different voivodeships was also intended to reflect potentially different insulation levels and thus the efficiency of generation.



**Table 1.** Characteristics of each analytical scenario.


**Table 1.** *Cont*.

<sup>1</sup> Agricultural activity profile: 01.11.Z—growing of cereals, leguminous crops and oil plants for seeds, except rice; 01.13.Z—growing of vegetables and melons, roots and tubers; 01.19.Z—growing of other non-perennial crops; 01.43.Z—raising of horses and other equines; 01.46.Z—raising of pigs; 01.47.Z—raising of poultry; 01.50.Z—mixed farming; 01.62.Z—support activities for farm-animal production. <sup>2</sup> Tariff group: The first character (C, B) refers to the tariff type, C—low voltage, B—medium voltage; the second character (1 or 2) refers to the installed capacity level, 1—up to 40 kW, 2—above 40 kW; the third character (1, 2 or 3) indicates the number of time zones; the fourth character, if any, indicates how to account for the time zones, a—division into peak and off-peak, b—division into day and night. <sup>3</sup> PVPP—photovoltaic power plant; SHPP—small hydro power plant; WPP—wind power plant; BMPP—biomass power plant; BGPP—biogas power plant. <sup>4</sup> min\_s—minimum energy storage capacity; expert level—energy storage capacity —expert recommendation; optimal level—energy storage capacity — optimal value; max\_s—maximum energy storage capacity.

> For the simulation and research, sample energy storage facilities (batteries) [47] with real parameters and operation profiles were mapped in the structures of cooperatives. The parameters of a minimum and maximum battery capacity and computational step were selected for each cooperative. For modeling, it was assumed that the change in the nature of the storage operation (charging/discharging) could occur at hourly intervals. Furthermore, it was assumed that unlimited charging and discharging is possible throughout the 24 h period. Recommendations from an energy storage expert were also used in the analyses. The expert selected capacity parameters and charging and discharging powers based on the demand-supply profile of each of the five simulated cooperatives and at the request of the research team. A summary of the parameters is shown in Table 2. This data served as a comparative element for the energy storage (battery) capacities determined during the optimization process, used further in the analysis, and presented in Table 1.

**Table 2.** Energy storage parameters.


#### *3.3. The Optimization Model*

The results presented in this paper were obtained on the basis of data from a simulation of a dedicated mathematical model. The mixed-integer programming technique [48] was used for modeling [49]. GLPK software was used for modeling, particularly the shared high-level GMPL language (this is an open-source software) [50]. COIN-OR/CBC software (also an open-source software) was used to solve the individual optimization tasks [51]. The basic assumptions of the model are discussed below, and parts of the model in the GMPL language are illustrated.

The input data for the model comprised a two-year horizon data in an hourly granulation. The calculation sessions used real data from several dozen consumers from different

billing tariffs. The real two-year generation profiles of the following electricity sources were used: a small hydro-power plant, a wind-power plant, a photovoltaic power plant, wastewater- and biomass-based biogas plants.

As the research progressed, the model described in the authors' earlier joint paper was developed [17] by adding an actual electricity store (battery) to it which, through appropriate parameterization, offered the possibility of being used in two scenarios. The first scenario assumed that the cooperative had no electricity storage (i.e., it had a storage with a maximum capacity of 0 kWh). The task was to select the optimum production mix for the pre-set demand, minimizing the energy not taken from the virtual network deposit and purchased from the network. The energy demand, depending on the calculation scenario, was created by individual consumers or aggregated consumers within predefined cooperatives. The energy mix is to be understood as the vector of discrete factors scaling the generation profiles of the energy producers considered. The coordinates of this vector are fixed during the optimization period. The business process modeled connected the consumers with the sources on a proprietary basis (the consumer was the source owner/prosumer). The first scenario is consistent with that already analyzed in the research team's previous article [17]. After solving the task without the real storage (with 0 kWh capacity storage), tasks were solved where the use of batteries was possible for the generation structure obtained in the first scenario. Properly produced energy could be a discrete multiple of the profile adopted.

The energy balance equation in the GMPL modeling language is presented in Algorithm 1.

#### **Algorithm 1.**

```
subject to def_EnergyBalance{h in Hours}:
EnergyDemand[h]
=
BuyFromNetwork[h]
+
sum{e in EnergySources} Production[e,h]
+
PickUpFromNetwork[h] - SendToNetwork[h]
+
PickUpFromBattery[h] - SendToBattery[h]
;
```
*EnergyDemand[h]* is the energy demand at hour *h*; *BuyFromNetwork[h]* is the energy purchase at hour *h*; *Production[h]* is the energy production at hour *h*; *SendToNetwork[h]* is the energy sending at hour *h* and *PickUpFromNetwork[h]* is the energy collected at hour *h*; *SendToBattery[h]* is the energy sending at hour *h to the real battery*; and *PickUpFromBattery[h]* is the energy collected from the real battery at hour *h.*

The model assumes that it is not possible to simultaneously send energy to the real battery and collect energy from it in the same hour *h*. The relevant model equations are in Algorithm 2:

#### **Algorithm 2.**

```
subject to constr_SingleComponentBatteryFlow{h in Hours}:
SendToBatteryIndicator[h] + PickUpFromBatterylndicator[h] < = 1;
```
The *SendToBatteryIndicator[h]* and *PickUpFromBatterylndicator[h]* variables are binary variables indexed by the hours of the optimization horizon, which takes the value 1 for non-zero values of the corresponding real variables and the value 0 for zero flows.

The algorithms modeling the operation of a real storage of energy produced by prosumers are in Algorithm 3:

#### **Algorithm 3.**

```
subject to def_EnergyBattery{h in Hours}:
Battery[h] =
if(h=1) then
0
else
(
Battery[h-1]
PickUpFromBattery[h]
+
SendToBattery[h]
);
```
In the analyses, it was assumed that, for *h=1*, i.e., at the beginning of the optimization, the battery was not charged, i.e., *Battery[0] = 0*.

The optimization objective function was the sum of two components—energy taken from the network and energy produced but not consumed. In Algorithm 4, the optimization was to minimize the following objective function.

#### **Algorithm 4.**

```
minimize objective:
sum{h in EndsOfBillingPeriods}
Storage[h]
+
sum{h in Hours}
BuyFromNetwork[h]
;
```
The *EndsOfBillingPeriods* set covered the last hours of billing periods.

The optimization covered a two-year horizon and the results presented refer to the first year of optimization.

#### **4. Results and Discussion**

The effect of energy storage can be considered on multiple levels. Given the availability of the actual metering data, the authors simulated the effect of energy storage separately for the specific five energy cooperatives and five energy cooperatives of different sizes, for which a random selection of members was made to obtain reference scenarios and conclusions. The results of the storage effect were shown in the volumetric dimension. The omission of financial aspects introduces universality into the approach, as it avoids the lack of offer transparency and avoids comparisons with electricity market prices specific to a particular country.

#### *4.1. Simulation Results for Dedicated and Reference Agricultural Energy Cooperatives*

A simulation was carried out in which the capacity of the energy storage operating within the cooperative was increased from 0 kWh to 50 MWh, with increments of 100 kWh to 1000 kWh, 500 to 10,000 kWh and 1000 kWh to 50,000 kWh. Each time, the charging power as well as the discharging power was assumed to be equal to 10% of the total capacity expressed in kW.

As a result of the simulations of the behavior of five agricultural energy cooperatives, it is possible to evaluate and analyze the results for specific configurations, taking into account the construction and operation of a real energy storage with the capacity and making it possible to achieve half of the maximum optimization effect. The rationality of choosing the storage capacity, obtaining the desired measurability of the storage effect

and the impact on the Demand Side Management (DSM) is also dictated by the results of available studies [52,53]. An illustration of the results is shown in Figure 1.

**Figure 1.** Simulation results of the optimization effect of the sum of energy taken from the network and energy remaining in the virtual network deposit after the billing period, depending on the battery capacity for individual energy cooperatives.

The results allow the following conclusions to be drawn:

	- -6000 kWh; CP1; half of the average effect: 16.5%;
	- -900 kWh; CP2; half of the average effect: 8.5%;
	- -300 kWh; CP3; half of the average effect: 14.9%;
	- -6000 kWh; CP4; half of the average effect: 9.3%;
	- -6500 kWh; CP5; half of the average effect: 8.9%.

The next stage of the analysis assumed a simulation of the effect of a real energy store for reference scenarios reproducing a random drawing of cooperative structures while maintaining the criterion of a specified number of members. For each such reference cooperative, a simulation was carried out in which the capacity of the energy storage within the cooperative was increased from 0 kWh to 50 MWh, with increments of 100 kWh to 1000 kWh, 500 to 10,000 kWh and 1000 kWh to 50,000 kWh. Each time, the charging power, as well as the discharging power, was assumed to be equal to 10% of the total capacity, expressed in kW. The results are shown in Figure 2.

**Figure 2.** Simulation results of a battery addition effect depending on the battery capacity for reference energy cooperatives. Also seen are measurement points and an average value for cooperatives with a specified number of members.

The simulation results are illustrated in Figure 2, which shows the percentage of the cooperative objective function depending on battery capacity. Solid lines represent the average effect of the measurement points obtained. The condition obtained reflects the effect of providing the cooperative with an energy storage relative to the condition before its installation (capacity: 0 kWh, objective function 0%). The results for cooperatives with different numbers of members are marked with points in different colors. The solid line corresponds to the average measurement points for cooperatives with a specified number of members.

The results allow the following conclusions to be drawn:


In addition, a detailed analysis of the cooperative size scenarios presented in Table 1 and Figure 2 allows the following conclusions:

	- -2000 kWh; size: 10 members; half of the average effect: 10.6%;
	- -5000 kWh; size: 20 members; half of the average effect: 11.0%;
	- -6500 kWh; size: 30 members; half of the average effect: 11.4%;
	- -8000 kWh; size: 40 members; half of the average effect: 13.8%;
	- -8500 kWh; size: 50 members; half of the average effect: 15.8%.

**Figure 3.** Simulation results of the optimization effect of the sum of energy taken from the network and energy remaining in the network deposit after the billing period, depending on the battery capacity and the number of cooperative members. Also seen is the average effect and battery capacity per average effect.

#### *4.2. Results and Evaluation of the Volumetric Effect of Energy Storage*

The simulation of the energy storage process was carried out according to two scenarios. Respectively, they assumed the evaluation of the impact of real energy storage in CP1 – CP5 agricultural energy cooperatives and reference models of one hundred energy cooperatives with population sizes from 10 to 50 members. Due to the dynamic development of the storage sector and the potential difficulty in rationally estimating the current and appropriate level of capital outlays on storage construction, the analyses focused exclusively on the volumetric effect of storage. The results for both scenarios are presented in Tables 3 and 4.


**Table 3.** Volumetric effects of real energy storage in cooperatives (CP).

**Table 4.** Volumetric effects of real energy storage in reference cooperatives with varying numbers of members (Mxx).


The analysis of the results allows the following conclusions to be drawn:


#### *4.3. Application of Decisions Trees to Assess the Effect of Energy Storage*

Based on the analysis of the profitability of specific cooperatives CP1-CP5, it was not possible to draw conclusions on regularity. For this reason, the focus was on serial randomized experiments. The results of serial experiments, in which the composition of cooperatives was randomized, were analyzed using decision trees [55] available in the R-Project software [56]. The relevant information was obtained in two modeling scenarios.

The first scenario assumed that the variable modeled was the average effect of the savings achieved after using a battery with a capacity equivalent to the half of the effect, discretized to three values: WeakImpact, MediumImpact and StrongImpact. In the experiment, random cooperatives with varying numbers of members achieved different types of percentage savings. In terms of savings achieved, cooperatives were flagged with the WeakImpact flag if the benefit of the battery use ranked in the lower <sup>1</sup> /3 of all possible percentage savings achieved during the series of calculations. The StrongImpact flag was used to designate cooperatives for which <sup>1</sup> /3 of the highest savings were achieved, and all others were marked with MediumImpact.

The following variables were used as explanatory variables: the number of members of the cooperative (NumberOfMembers, which could assume one of the following values 10, 20, 30, 40, 50), the percentage total share of wind and photovoltaic sources (marked as WindAndPV) and other sources (marked as OtherEnergySourceThanWindAndPV).

Figure 4 shows a decision tree describing the class of savings size depending on the type of sources used and the number of cooperative members. The set of observations was divided into eight segments by the decision-tree algorithm. The leftmost branch of the tree was considered to explain the reading method. It ends with a green leaf marked MediumImpact. In the classification process, observations for which the number of cooperative members was less than 35 and, at the same time, fewer than 25 were included in this segment. In addition, the share of wind and photovoltaic sources in the production was at least 70%. This leaf accounted for 22% of all observations. In all, 75% of the population of this leaf constitute MediumImpact class elements, and 7% and 17% are StrongImpact and WeakImpact class elements, respectively.

The rightmost leaf was marked WeakImpact. In the classification process, observations for which the number of cooperative members was greater than or equal to 35 and in which the share of sources other than wind and photovoltaic in the production was at least 28% were included in this segment. This leaf accounted for 13% of all observations. The elements of the MediumImpact class constitute 6% of the population of this leaf, and the elements of the StrongImpact and WeakImpact classes constitute 38% and 56%, respectively. The set of decision rules of the tree visualized in Figure 4 is recorded in Table 5.

**Figure 4.** Decision tree quantifying the magnitude of potential benefits after the implementation of a battery corresponding to the "half effect" relative to the distribution of sources and number of members.

**Table 5.** Decision-tree rules quantifying the magnitude of potential benefits after the implementation of a battery corresponding to the "half effect" relative to the distribution of sources and number of members. The color scheme used in column two corresponds to that of the leaves in Figure 4.


The second modeling scenario did not include the size of cooperatives in the explanatory variables. Relevant rules are more aggregated than those shown in Figure 4 but are more easily interpretable in business terms. They show that the greatest effect from using a real battery can be obtained if the percentage production from wind and PV sources is in the 72% to 83% range (StrongImpact). This is illustrated in Figure 5. The MediumImpact level is reached when the percentage of production share from wind and PV sources is greater than 83% (other sources generate no more than 17%), and the effect is the lowest when the share of production from wind and PV sources is less than 72% (other sources generate at least 28%).

**Figure 5.** Simulation results in the regression-tree modeling without a variable specifying the number of cooperative members.

It seems that a good approximation of the dependencies obtained is the statement that the maximization of the effect from the application of batteries takes place when the production mix is 75% for wind and PV sources and 25% for other sources. This conclusion seems reasonable, given that sources considered other than PV and wind have a more stable generation profile. Thus, the sources with unstable profiles interact more effectively with real energy storage systems.

#### **5. Conclusions**


maximized when up to 75% of the generation is from wind and photovoltaic sources, and only 25% from hydro, biogas and biomass sources.

7. The real storage of energy within energy cooperatives, integrating the already optimally selected participants, does not result in a significant improvement in the objective function. The reduction in energy intake from the network and the increase in self-consumption always occur but, in the authors' opinion, the scale of the phenomenon is not satisfactory. The storage analysis should be carried out individually for each configuration of the cooperative. This conclusion indicates that the main purpose of the article, which was to present the results of the assessment of the actual impact of energy storage on operational efficiency, was fairly presented. Energy storage is therefore an interesting area for further in-depth exploration and research, and sensitivity analyses should take into account (i) different charging and discharging time regimes; (ii) mapping investment outlays and operating costs as a function of time; (iii) leveled cost of electricity (LCOE); (iv) predicted improvement in storage efficiency due to advancing technology, quantum batteries [57,58] and propensity to change social behavior [59]; and (v) the fact that effect and prices in the dual market for energy and capacity seem to be particularly valuable. These topics will be the subject of further research by the authors.

**Author Contributions:** Conceptualization, J.J., M.K. and M.S.; methodology, M.K.; formal analysis, J.J., M.K. and M.S.; investigation, J.J., M.K. and M.S.; resources, M.S.; writing—original draft preparation, J.J., M.K. and M.S.; writing—review and editing, J.J.; visualization, M.K.; supervision, J.J. and M.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data available in a publicly accessible repository that does not issue DOIs. The data presented in this study can be found here: https://drive.google.com/drive/folders/ 1JKvGCnHBZabmodsTrMUuxp6eXhagI0K0?usp=sharing.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Production Profile of Farms and Methane and Nitrous Oxide Emissions**

**Zofia Koloszko-Chomentowska 1,\*, Leszek Sieczko <sup>2</sup> and Roman Trochimczuk <sup>3</sup>**


**Abstract:** The negative impact of agricultural production on the environment is manifested, above all, in the emission of greenhouse gases (GHG). The goals of this study were to estimate methane and nitrous oxide emissions at the level of individual farms and indicate differences in emissions depending on the type of production, and to investigate dependencies between greenhouse gas emissions and economic indicators. Methane and nitrous oxide emissions were estimated at three types of farms in Poland, based on FADN data: field crops, milk, and mixed. Data were from 2004–2018. Statistical analysis confirmed the relationship between greenhouse gas emissions and economic performance. On milk farms, the value of methane and nitrous oxide emissions increased with increased net value added and farm income. Milk farms reached the highest land productivity and the highest level of income per 1 ha of farmland. On field crops farms, the relationship between net value added and farm income and methane and nitrous oxide emissions was negative. Animals remain a strong determinant of methane and nitrous oxide emissions, and the emissions at milk farms were the highest. On mixed farms, emissions result from intensive livestock and crop production. In farms of the field crops type, emissions were the lowest and mainly concerned crops.

**Keywords:** agricultural production; emission; methane; nitrous oxide; dairy cows; field crops; agricultural production; family farm income; land productivity

#### **1. Introduction**

The 2030 Agenda for Sustainable Development, adopted in 2015, is a comprehensive plan of development for the world established by the United Nations (UN). All UN member states committed to taking action toward creating adequate living conditions and conditions for economic progress while simultaneously protecting the environment and counteracting climate change. Climate change is progressing due to increased greenhouse gas (GHG) emissions, including carbon dioxide (CO2). In 2020, annual CO2 emissions increased by 20% globally compared to 2005 [1]. East Asian and Pacific countries emitted more CO2 than in 2005 (by 50%), whereas emissions decreased in North America (by 13%) and in European and Central Asian countries (by 9%) [1]. The amount of greenhouse gases emitted annually by the EU decreased by 12% compared to 2010, while Poland emits over 400 million tons of greenhouse gases annually, which makes up 9.8% of the EU's emissions [1]. It is necessary to take action over the next several years to reduce the risk of irreversible effects of climate change, particularly since the Earth will continue to react to increases in greenhouse gas emissions for a long time after they are reduced [2]. Increasing the use of renewable energy sources is one measure that can contribute to the reduction of greenhouse gas emissions. Poland is involved in actions aimed at limiting climate change that are being undertaken by the international community. It is one of the signatories of the UN Framework Convention on Climate Change (UNFCCC) since 1992 and the Kyoto Protocol since 2002 [3].

**Citation:** Koloszko-Chomentowska, Z.; Sieczko, L.; Trochimczuk, R. Production Profile of Farms and Methane and Nitrous Oxide Emissions. *Energies* **2021**, *14*, 4904. https://doi.org/10.3390/en14164904

Academic Editors: Vitaliy Krupin and Roman Podolets

Received: 15 April 2021 Accepted: 5 August 2021 Published: 11 August 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Agriculture is one of the sectors of the economy that has a strong relationship with the natural environment. The technological, biological, and organizational progress that is being made affords access to increasingly modern production technologies. This leads to improvements in the technical and economic efficiency of agricultural production. However, these changes are generating a series of threats to the natural environment. In relation to the growth of the global population and a growing demand for food, there is pressure to increase the magnitude of agricultural production. Today, technical capabilities with regard to increasing the scale of production are not a limitation, as this process is accompanied by economic benefits; however, an environmental barrier does arise. The negative impact of agricultural production on the environment is manifested, above all, in the emission of greenhouse gases (GHGs), mainly nitrous oxide (N2O) and methane (CH4) [4–6].

The Kyoto Protocol lists CO2 as one of the gases that has an influence on the greenhouse effect. This is the most important factor in climate change, and is covered in most studies. However, some researchers are voicing the opinion that basing estimates solely on CO2 emissions and omitting other gases in the balance associated with agriculture, particularly in rural and urban–rural municipalities, leads to underestimation of GHG emissions from Polish agriculture [7–9]. Research by Wi´sniewski [7] shows that over half of the total emissions from agriculture in Poland is associated with animal raising and breeding. This is confirmed by the results of many studies. GHG emissions from agriculture in Africa are showing some of the highest rates of growth in the world, the greatest source of which is animal production on farms [6]. The case is similar in EU member states, where the largest amount of emissions also comes from animal production. During 2004–2017, GHG emissions were highly concentrated in several EU member states; these were the countries with the most developed agriculture: France, Germany, Spain, and the United Kingdom [10].

There is a strong emphasis on the need to reduce greenhouse gas emissions in agriculture and to incorporate agriculture in actions against unfavorable climate change [2,11]. According to the Ministry of Climate, in Poland in 2018, agriculture was responsible for 8% of greenhouse gas emissions (in CO2 equivalent) with respect to the base in 1988 [12]. Although it is necessary, reducing GHG emissions in this sector remains an enormous challenge. This is because there is a specific conflict of interest in this area. Agricultural holdings are subject to competition in the food market, and reconciling economic and environmental interests is a problem. Unfortunately, the magnitude of GHG emissions from agriculture is disturbing. In Poland in 2018, a 7.2% increase in GHG emissions from agriculture was recorded with respect to 2015 [13]. The search for effective tools for production technology management in order to consume fewer resources and reduce the environmental impact is ongoing [14]. It seems that only deep changes in the structure of the entire agri-food system can reduce greenhouse gas emissions in the agricultural sector [15]. This is not only about the practices employed in agricultural production, but also changes in consumers' nutritional habits; for example, research conducted in Mediterranean regions indicates that reducing meat and dairy consumption by 40% could reduce GHG emissions by 20–30% [16].

Transforming the economy into a low-emissions economy is currently one of the most important challenges facing the modern world. A circular, low-emissions economy plays a critical role in the development of agriculture, as it is an opportunity to improve both the quality of the environment and economic well-being. The social aspect of the low-emissions economy is highlighted. Limiting greenhouse gas emissions brings about benefits in terms of human health regardless of the level of prosperity, as the benefits apply to both rich societies and less affluent ones [17]. The economic dimension of the relationship between agriculture and climate change is also important. A slight reduction in GHG emissions resulting from the growth of value added in agriculture and renewable energy was observed in studies conducted in Pakistan [18]. The authors of that research suggest that increasing the value added of agriculture and consumption of renewable energy could counterbalance the increased GHG emissions resulting from the consumption of coal-generated electricity. Zafeiriou et al. [19] obtained divergent results in their research on the relationship between greenhouse gas emissions from agriculture and per capita income in the agricultural sectors of different EU countries. The results indicated that if CO2 emissions rise, so would income from agriculture, which was confirmed in the case of Spain. However, the authors expressed a reservation regarding the nonlinear relationship between agricultural income and CO2 emissions. Other studies indicate a positive influence of direct foreign investment in agriculture on the CO2 emission equivalent in developing countries [20]. The economic aspects of greenhouse gas reduction are rarely raised in studies. A report by the Centre for Climate and Energy Analyses unequivocally shows that reducing methane and nitrous oxide emissions from agriculture in Poland causes changes in farmers' level of production and income, and should be considered through the lens of economic effects [21]. At the same time, the report's authors are aware of how difficult it is to reach a compromise between these two objectives.

The assessment of agriculture's environmental impact is part of the concept of sustainable development. Studies concerning the impact of farms with different production profiles on the environment are an important part of this. It seems that such assessment is important because the impact of an agricultural holding on the environment depends on its specialization. Specialized farms are the ones that determine the basic trend of transformation in Polish agriculture. Specialization is a factor that fosters improvement of farming efficiency; however, there are environmental limitations linked to the growth of such farms. In such cases, activity is associated with the concentration of resources and intensity of production, so the environmental impact assessment is multi-dimensional. Various environmental and economic sustainability indicators are taken into account in such assessments [22–27]. The choice of indicator generally depends on the availability of data. In most assessments, greenhouse gas emissions are either omitted or treated as a side note. The relationships between environmental practices and economic results have also been insufficiently investigated. The present paper broadens the knowledge in this scope. Farmers, even those with the highest environmental awareness, will always be motivated by an economic objective in their activity. Thus, it is necessary to account for economic aspects in analyses of greenhouse gas emissions. Doing so can provide a broader picture of the dependencies existing between the farmer's choice of agricultural practices and the realization of the environmental objective, i.e., reducing methane and nitrous oxide emissions from agriculture.

The goals of the study were to estimate methane and nitrous oxide emissions at the level of an individual farm and indicate differences in emissions depending on the type of production, and to investigate dependencies between greenhouse gas emissions and economic indicators. The authors' intent is to present estimates of CH4 and N2O for three types of specialized agricultural holdings and to indicate the relationships between the economic objectives that motivate farms and the environmental objectives that arise from concern for the natural environment.

#### **2. Methodology**

Data concerning farms were obtained from the Farm Accountancy Data Network (FADN), published by the Institute of Agricultural and Food Economics, Polish Research Institute [28]. The data used in this research are not available in other databases. They concern agricultural accountancy, and hence are focused mainly on economic categories and the financial situation of individual farms; however, they can also be used for environmental analyses [29–31]. For the purposes of this study, we adopted the methodology described by Wi´sniewski [7], who proposed assessing the magnitude of greenhouse gas emissions based on data from public statistics. The proposed solution complies with the methodology and standard indicators of the Intergovernmental Panel on Climate Change [32] and accounts for emission indicators developed by the National Centre for Emissions Management [33]. Other authors have also used data from public statistics to estimate emissions, including methane and nitrous oxide emissions [34–36].

Although the applied methodology is a simplified solution, it makes it possible to utilize generally available data on agricultural holdings and assess the impact of farming on the environment. Such a solution makes it possible to assess the variability of emissions and compare farms with respect to criteria such as farm size, production system, and type of production. Dick et al. [37] point to the advantages of such a solution, mainly from a practical perspective. Above all, it enables farmers to apply the best practices, select a method of production, and choose the means of its implementation.

The data come from 2004 to 2018. Three types of agricultural holdings were considered in the analyses: those that specialize in field crops, specialize in milk production (dairy cattle), or have a mixed production profile. These are the main types of farms in Poland. Data on the number and basic characteristics of farms are presented in Tables A1 and A2 (Appendix A) It should be noted that the number of farms changes every year, which is due to the selection of the sample included in the FADN system. Every year, some farms remain outside of FADN's area of observation, and other farms enter the sample.

Research was focused on the three main sources of greenhouse gas emissions, emitted directly over the course of agricultural production: gastrointestinal fermentation in farm animals (main source of methane emissions), animal feces (source of methane and nitrous oxide emissions), and nitrous oxide emissions from the use of mineral fertilizers.

Estimates of the magnitude of methane and nitrous oxide emissions from animal production were made based on the number of livestock and emission coefficients. In the case of cattle, available national gut fermentation CH4 emission coefficients applied by KOBiZE are used to prepare annual inventory reports. They are prepared based on daily energy demand for selected categories of cattle and coefficients of conversion to methane (share of energy in fodder converted to methane). Methane emission indicators from the livestock's gut fermentation is estimated based on the more general, default indicators recommended by the IPCC [32]. The level of nitrous oxide is estimated based on default indices of nitrogen content in animal feces and default N2O-N emission coefficients for different methods of animal feces management [32]. The following emissions coefficients were applied (kg per animal per year): CH4 from gastrointestinal fermentation: dairy cows, 122.0; other cattle, 49.65; swine, 1.5; CH4 from feces: dairy cows, 11.87; other cattle, 2.15; swine, 3.07; nitrogen excreted in feces: dairy cows, 70.26; other cattle, 49.95; swine, 30.22 [7]. In FADN data, animals are counted as livestock units, which was why there was a need to convert these units into physical headcounts. This was done according to coefficients for conversion of cattle and swine, with the following coefficients adopted: dairy cows, 1.0; swine, 0.25; other cattle, 0.40 (mean value determined for heifers and calves) [38]. Poultry was omitted in the calculations due to the lack of IPCC guidelines.

The amounts of methane and nitrogen emissions were then calculated per 1 ha of farmland. The reference to farmland area was made for two reasons. First, when conducting a comparative analysis of three types of specialized farms, one needs to accept a single point of reference, and that is farmland area. Second, this made it possible to investigate dependencies between methane and nitrous oxide emissions and economic results, which was the intended goal of this work.

The average consumption of fertilizers per ha of farmland was adopted as the basis for estimating nitrous oxide emissions from mineral fertilizers. There is no information in the generally available FADN data about the consumption of mineral fertilizers, which is why this quantity was estimated indirectly based on average NPK consumption at individual farms in the country according to the Central Statistical Office [39]. The quantity of NPK consumption in the studied agricultural holdings was corrected by the indicator representing the general production and economic advantage of the studied farms over individual farms in Poland, collectively, as applied by the Institute of Agricultural and Food Economics [40]. This indicator was determined for every year based on a comparison of the production value per 1 ha of farmland of the most important products (basic cereals, potatoes, milk, and pig livestock) of the studied farms, with the production value of these products for the collective of farms, according to the Central Statistical Office, accepted as 1. It is accepted that farms that conduct agricultural accountancy achieve higher production and economic results than average farms in the region and in the country.

When estimating amounts of emissions from the use of mineral fertilizers, the default nitrous oxide emission coefficient of 0.01 kg N2O-N per 1 kg N was accepted. The mass of nitrogen originating from the application of mineral fertilizers was corrected by the amounts of ammonia and nitrous oxides emitted [7].

The following indicators were also applied to evaluate the economic situation of agricultural holdings: net value added (PLN ·AWU<sup>−</sup>1), family farm income (PLN), family farm income per 1 ha of farmland (PLN), and land productivity per 1 ha of farmland (PLN).

The arithmetic mean, minimum, maximum, and standard deviation were used to present results from 15 years of observation. Based on economic indicators originating from the described farms and emission values calculated for the selected greenhouse gases, an attempt was made to present the variation of these indicators over the course of those 15 years. Thirteen features describing the economic and agrarian characteristics of farms, along with the number of farms with a given agricultural production profile taking part in FADN studies, were taken as variables and subjected to reduction during analysis. The number of farms is not a feature associated with emissions, and in normal studies with repetitions, it should not be taken for analysis. Here, however, FADN studies were based on a variable number of farms, therefore, this feature could justify variation within the very short time period of one year. Ten indicators of GHG emissions were also taken as variables for analysis. The set of input data consisted of 23 features, representing the dimensions of the three described types of farms over 15 years, which were treated as objects in the analysis (Table 1).


**Table 1.** Values of variables used in the analysis (for three types of farms).

Source: Own calculation based on FADN data [28].

Three independent analyses were carried out for each type of farm. Factor analysis was conducted using principal component analysis (PCA) [41,42]. To facilitate the interpretation of results, varimax rotation was applied. This involves rotation of the X- and Y-axes (linear combination) so as to maximize the variance of loadings between factors and minimize their variance within the new factor called a component here.

#### **3. Results**

The level of greenhouse gas emissions was dependent on the production profile and was characterized by high variation during the studied period (Figure 1). Farms specializing in milk production emitted the most CH4 and N2O. This is the result of high livestock density and intensive production technology. Farms specializing in milk production surpassed other types of farms in terms of the amount of income from the farm and land productivity (Table A2, Appendix A). At field crop farms, the levels of methane and nitrous oxide emissions were the lowest among the studied farms. This is due to the specialization adopted and consistent reduction of animals on the farm. During the period of study, changes in the levels of CH4 and N2O occurred at all farms and were associated with organizational changes at the farms. There is a positive correlation between methane and nitrous oxide emissions and economic results measured at the level of family farm income. Milk farms reached the highest land productivity and the highest income level per 1 ha of farmland.

On farms that specialize in field crops, the average methane emissions amounted to 5.96 kg·ha−1, varying within a range of 0.25 to 11.3. Within the studied time interval, several periods of lesser and greater CH4 emissions can be distinguished (Figure 1A). After a period of slight decrease in the level of emissions during 2005–2006, there was an increase during the next two years (2008–2009) to a level of 11.31 kg·ha−1. In the following years, a declining tendency can be seen, and in 2018, total CH4 emissions per 1 ha of farmland was more than three times lower than in 2004. During the studied time interval, the period of 2010–2012 is noteworthy as greenhouse gas emissions were very low during that time. The factor responsible for this was the selection of farms that were within FADN's area of observation during those years. These were much larger farms and the average area of farmland was twice as large as in other years.

The level of emissions should also be considered against the backdrop of organizational changes in agricultural holdings, particularly with regard to animal production. In 2004, the average number of animals on a farm in livestock units amounted to 3.51 LU (1.6 of cattle and 1.91 of swine). This number decreased every year after that (R2 = 0.7706). In 2018, the number of animals was reduced to 0.09 LU of dairy cattle (which can be considered as total elimination), 0.55 LU of other cattle and 0.43 LU of swine. Organizational changes in the studied group of farms indicate progressing specialization. These farms specialize in field crops. The first few years were a period of adaptation to the selected production profile and many holdings continued to raise animals. In every year that followed, farms reduced animal production in favor of field crops according to the specialization they adopted.

In the case of dairy cattle farms, CH4 emissions were substantially higher ranging from 119.90 to 157.73 kg·ha−<sup>1</sup> depending on the year. Throughout the entire period of study, the level of emissions stayed at a relatively constant level and systematically increased starting from 2013 (Figure 1B). The causes of this situation are understandable. These holdings specialize in milk production, and over the course of successive years, farmers increased their herds of dairy cows (R2 = 0.9663). The average number of cows on a farm in 2004 amounted to 10.69 LU, and in 2018, the number was 17.12 LU. This is the basic production herd, and in the case of farms specializing in milk production, the scale of production is fundamentally important in terms of farming economics. Besides cattle in the basic herd, other functional groups of cattle were also present, most likely constituting a replacement herd. This population of cattle also increased from year to year. In the case of this group of holdings, the total magnitude of emissions originated from cattle raising (there were no other animal species).

**Figure 1.** Emission levels of CH4 (kg·ha<sup>−</sup>1) at three types of farms: (**A**) Field crops; (**B**) Milk; (**C**) Mixed.

For holdings with a mixed profile, CH4 emissions were at a moderate level. If we accept the level of CH4 emissions at dairy cow holdings to be 1, then farms specializing in field crops were at a level of 0.045 on average during the studied period and mixed holdings were at a level of 0.57. During the studied period, the amount of CH4 emissions changed from 56.09 to 85.67 kg ha<sup>−</sup>1. The level of this gas exceeded 80 kg·ha−<sup>1</sup> in only four years (Figure 1C).

The amount and changes of CH4 emissions should be considered against the backdrop of the way animal production was organized. During 2004–2005, over half of CH4 emissions originated from dairy cattle, and the number of cattle was the greatest during those years. However, starting from 2006, the number of cows was successively reduced and herds of beef cattle were enlarged. These changes are reflected in the structure of CH4 emission sources. In 2018, over 65% of emissions originated from the raising of beef cattle. In total, cattle were responsible for 85–90% of CH4 emissions depending on the year. In terms of swine herds, changes in herd populations were small, and they were responsible for approximately 12% of methane emissions on average during the studied period; this level declined in the years that followed.

The analysis performed indicates that holdings with a mixed profile raised both cattle and swine, however, in recent years, they became more oriented toward raising beef cattle. These changes were reflected in greenhouse gas emissions. Regardless of the production profile, gastrointestinal fermentation in cattle is mainly responsible for methane emissions. Cattle were responsible for 82.5% of methane emissions on mixed farms and up to 92% on dairy farms. Accordingly, 8–17.5% of methane emissions originated from animal feces. Animal production is also a source of nitrous oxide emissions via feces. N2O emissions were higher for farms with larger animal herds (Figure 2).

Farms that specialize in milk production produce the most nitrous oxide emissions, taking into account nitrous oxide from animal feces and mineral fertilization at a level of 90.06 kg N2O ha−<sup>1</sup> (Figure 2B). Meanwhile, on mixed farms, the value of nitrous oxide emissions was 78.96 kg N2O·ha−1, and on farms specializing in field crops it was 9.65 kg N2O ha<sup>−</sup>1. These data indicate that cattle are the main emitters of not only methane but also nitrous oxide. On dairy farms, 93–95% of N2O emissions originated from animal feces. On field crop farms, N2O emissions mainly originated from mineral fertilization. Animals were kept solely for the family's own needs (0.09 LU dairy cattle and 0.55 LU other cattle in 2018).

Data on nitrous oxide emissions from the application of mineral fertilizers indicate that the greatest emissions came from farms that specialize in milk production. This is the result of intensive fertilization of cultivated plants. Corn, which constitutes the main feed base for cattle, is dominant in the crop structure. During the studied period, the area of corn cultivation increased from 0.3 ha in 2004 to 14.24 ha in 2018, while the area of cereals changed from 5.39 ha to 7.49 ha. During this period, consumption of mineral fertilizers increased from 163 to 271 kg NPK·ha<sup>−</sup>1.

The amount of nitrous oxide emissions from mineral fertilizers on mixed farms was 65% of the amount on dairy cattle farms. During the studied period, the level of N2O emissions was variable, and it is difficult to unequivocally identify a trend (Figure 2C). The highest level of emissions was recorded in 2006 at 86.31 kg N2O ha−<sup>1</sup> and the lowest in 2012 at 72.64 kg N2O ha−<sup>1</sup> per farm. Consumption of mineral fertilizers increased during the studied period from 132 to 152 kg NPK·ha−<sup>1</sup> per farm.

The lowest level of N2O emissions from mineral fertilizers was noted on farms that specialize in field crops (Figure 2A). Emissions from field crop farms amounted to 35% of dairy farm emissions and 55% of mixed farm emissions. Field crop farms applied less mineral fertilization than the other studied groups. Fertilizer consumption ranged from 76 to 98 kg NPK ha−<sup>1</sup> depending on the year.

**Figure 2.** Emission levels of N2O (kg·ha<sup>−</sup>1) at different types of farms: (**A**) Field crops; (**B**) Milk; (**C**) Mixed.

Reducing the 23 dimensions representing the primary features introduced for analysis in the case of dairy cattle farms, 21 dimensions (absence of swine herd) distinguished just one principal component responsible for 91.35% of total variation. This demonstrates that these farms are highly specialized in this production. All undertakings associated with activities described by the studied features had an equally strong impact on emissions of selected greenhouse gases over the course of the 15-year period. After applying varimax rotation, two principal components were distinguished: PC1 (57.39%) and PC2 (38.26%) (Figure 3, Table A3, Appendix A). After varimax rotation, the assignment of the majority of features to PC1 remained unchanged with the exception of CH4 and N2O emissions per 1 ha.

**Figure 3.** Relationships of locations of studied sources of GHG emissions (Z1 .... Z4 Z7 .... Z10) and economic indicators (X1 .... X13) for farms with milk production in the studied years (Y2004 .... Y2018) in the space of the first two components, PC1 and PC2.

Similar to farms with a mixed production profile, field crop farms are characterized by diverse factors that influence total GHG emissions. Principal component analysis clearly differentiated the studied features into three principal components describing total variation. The first component was responsible for 41.19% of total variation and the features most strongly correlated with it included CH4 and N2O emissions from each group of animals, total CH4 and N2O emissions, and number of animals.

The second component explained 39.64% of total variation (Figure 4, Table A4, Appendix A) and was most strongly correlated with the following economic indicators: total family farm income (PLN) and income per 1 ha of farmland, as well as net value added (PLN·AWU<sup>−</sup>1), total output (PLN), farmland area (ha), energy (PLN), intermediate consumption (PLN), total inputs (PLN), and mineral fertilizers (PLN). The third component explained 15.64% of total variation and was most strongly correlated with land productivity (PLN ha<sup>−</sup>1) and total inputs (PLN ha−1).

**Figure 4.** Relationships of locations of examined indices (Z1 .... Z10) of GHG emissions and economic indices (X1 .... X13) for field crop farms in the studied years (Y2004 .... Y2018) in the space of the first two components, PC1 and PC2.

Mixed production farms are characterized by diverse factors influencing total CH4 and N2O emissions. Principal component analysis clearly differentiated the studied features into four principal components describing total variation. The first component explained 48.83% of total variation and was most strongly correlated with variables including farmland area (ha), livestock units (LU), total production (PLN), total inputs (PLN), mineral fertilizers (PLN), energy (PLN), intermediate consumption (PLN) and total inputs (PLN·ha<sup>−</sup>1) as well as land productivity (PLN·ha<sup>−</sup>1), net value added (PLN·AWU<sup>−</sup>1), CH4 and N2O emissions originating from cattle groups other than dairy cows, and total CH4 and N2O emissions. The second component explained 22.55% of total variation and was most strongly correlated with variables including livestock units (LU) and CH4 and N2O emissions from dairy cattle and swine (Figure 5, Table A5, Appendix A). The variation explained by the first two components amounts to 71.38% of the total variation. The next components distinguished in the analysis were PC3, explaining 14.95%. and PC4, explaining 8.33% of the total variation.

Statistical analysis confirmed the dependency between CH4 (Z8) and N2O (Z10) emissions and economic results: net value added (X11) and family farm income per 1 ha (X13). On dairy cattle farms, the value of CH4 and N2O emissions grew as the values of economic indicators increased. Net value added and family farm income (PLN·ha<sup>−</sup>1) were positively correlated with CH4 (r = 0.700 and 0.700) and N2O (r = 0.802 and 0.774) emissions (Table A7, Appendix A). On field crop farms, the dependency between net value added and CH4 and N2O emissions was negatively correlated (r = −0.814 and −0.785). Similarly, there was a negative dependency between family farm income (PLN·ha−1) and emissions of the studied greenhouse gases (r = −0.695 and −0.676) (Table A6, Appendix A). On mixed farms, the dependency between economic indicators and CH4 and N2O emissions was negative, except the dependency between net value added and CH4 emissions (r = 0.272) (Table A8, Appendix A).

**Figure 5.** Relationships of locations of studied GHG emission sources (Z1 .... Z10) and economic indicators (X1. .... X13) for mixed production farms in the studied years (Y2004 .... Y2018) in the space of the first two components, PC1 and PC2.

#### **4. Discussion and Conclusions**

The growing demand for food requires intensification of agricultural production, which has a negative impact on the environment. This impact contributes to depletion of energy carriers, global warming, and reduction of air quality [43,44]. In order to ensure sustainable development, we need to search for solutions that can conserve environmental values while enabling the achievement of economic goals. The agricultural ecosystem both emits and absorbs greenhouse gases, and because of this, we use the concept of net greenhouse gases [45].

The analysis results indicate that animals remain a strong determinant of GHG emissions. A particularly high level of emissions is associated with cattle raising, which was the case for dairy cattle holdings. The level of nitrous oxide emissions was also high, as a result of the application of intensive feed production technologies. This was also confirmed by principal component analysis (PCA). This indicates that on dairy cattle farms, the organization of both animal and plant production is completely subordinated to milk production. Emissions on mixed farms are the result of intensive animal and plant production. Meanwhile, on field crop farms, where animal production was successively reduced, emissions were the lowest and mainly pertained to crops. This was also confirmed by analyses conducted at the regional level [46]. In Poland, regions with a larger share of large agricultural holdings and animal production (the northeastern part of the country and the Wielkopolska region) are characterized by higher emissions levels [7,36].

The values obtained in this research are higher than those obtained by other authors. According to Wi´sniewski [7], 42% of emissions originate from gastrointestinal fermentation in rural and urban–rural municipalities. In their investigations of emissions from agriculture in Africa, Tongwane et al. [6] determined that gastrointestinal fermentation was responsible for over half of all emissions originating from agriculture. Studies conducted in Ireland showed that 49% of emissions originated from gastrointestinal fermentation [47]. However, it should be noted that, in general, greenhouse gas emissions are estimated based on data for an average agricultural holding in the region or country and include all greenhouse gases. Meanwhile, our studies examined commodity farms that apply intensive technologies linked to specialization of production. In this case, we are dealing with concentrated means of production and high livestock density. These farms are distinguished against a background of so-called average farms by significantly higher production and economic results, but at the same time, they exert greater pressure on the environment. This hypothesis was confirmed by the research of Wysocka-Czubaszek et al. [36] concerning CH4 and N2O emissions in Poland. According to those authors, 51% of CH4 and 37% of N2O is emitted by three voivodeships where there is intensive agriculture: the Masovian and Podlaskie voivodeships, leading producers of milk and beef, and another voivodeship characterized by intensive production of animals and plants. The release of large amounts of methane and nitrous oxide is therefore the result of specialization which is associated with the concentration of agricultural holdings' resources.

The economic results obtained for field crop farms are concurrent with the results obtained by Khan et al. [18]. Growth of net value added and farm income per 1 ha of farmland caused a reduction of CH4 and N2O emissions. Meanwhile, on dairy cow farms, dependencies between economic results and gas emissions are different, confirming the results of Zafeirou et al. [19]. In this case, as value added and farm income increase, so do CH4 and N2O emissions. Syp and Osuch [31] obtained similar results in their investigations of organic and conventional farms. In their research, higher productivity was found on milk farms and was associated with higher GHG emissions. The view that farms which have more animals (conventional farms had more animals than organic farms) emphasize economic objectives, and that productivity is prioritized over environmental objectives, was also confirmed.

The results obtained indicate that the direction of dependencies between greenhouse gas emissions and economic results is determined by the presence of animal production. particularly cattle. Cattle are responsible for the highest emissions of CH4 [48].

The example of the three types of agricultural holdings described in this study confirms the hypothesis of the relationship between specialization of agricultural production and CH4 and N2O emissions. Dairy farms are the most harmful to the environment. Compared to farms of other types (field crop and mixed), they emit the highest amounts of CH4 and N2O. This is the result of a high concentration of animals on the farm and intensive plant production for use as fodder. Farms of this type successfully implement their economic goals, with the highest net value added and farm income per area unit. Field crop farms are less harmful to the environment. Farms of this type have successively reduced their livestock production, resulting in lower CH4 and N2O emissions. In this case, the quality of the soil may deteriorate due to the lack of organic fertilization.

Intensive agriculture does not have to be a threat to the environment. Countries that have achieved sustainable agriculture have done so by developing large farms and a high level of mechanization [49]. It is expected that agriculture will satisfy the needs of the growing global population while contributing to the reduction of GHG emissions. Achieving this goal will require intensification of production with higher emissions per unit of land area but lower emissions per unit of agricultural production [50,51].

Reducing greenhouse gas emissions from agriculture requires the introduction of innovative technologies and tools to increase the efficiency of agricultural production. One effective method of limiting methane emissions is to use a cattle nutrition strategy. Studies confirm that methane emissions have been reduced as a result of the application of high-starch diets or exogenic enzymes. Supplementation with fats also yields good results. This indicates that appropriate diets can be implemented for dairy and beef cattle in order to reduce methane emissions without reducing productivity [52].

According to Hoglund-Isaksson et al. [53], the possibilities of reducing methane and nitrous oxide emissions from agriculture are limited and technological solutions are insufficient. Hence, they propose the introduction, by 2050, of institutional reforms and changes to human nutritional habits on a broad scale, in addition to the implementation of technological solutions. Meanwhile, Ockoet al. [54] believe that achieving a reduction of methane and nitrous oxide emissions by changing the human diet is less realistic than implementing technological strategies.

Specialization fosters the development of farms and builds competitive advantage. As research indicates, specialization is deepening, and economic goals are the decisive factor in the adoption of areas of specialization by agricultural holdings [55]. Farms with intensive animal production have the strongest impact with respect to the environment. Solutions that make it possible to reduce the pressure of agriculture on the environment while maintaining food security are already known. These are, above all, good agricultural practices, including no-till farming, breeding progress, and effective fertilizer management. Good management practices may reduce the burden on the environment and the costs of agricultural production. Economic instruments are also indicated for strategies to limit emissions, e.g., in the form of compensation for income lost due to reduced production intensity [56].

W ˛as et al. [21] presented several scenarios of reduced methane and nitrous oxide emissions based on data describing the Polish agricultural sector in the base year 2015. Changes in income levels are an important indicator from the perspective of analyzing the potential economic consequences of various scenarios of reduced greenhouse gases from agriculture. The process of reducing emissions in agriculture using currently known and available technologies is highly complex and inevitably leads to drops in production and income. According to these scenarios, the greatest drops in production and income are observed in the case of beef and dairy cattle. Plant production is the least sensitive to restrictions with respect to emissions.

The present research contributes to agricultural science and environmental economics by broadening the knowledge on the subject of relationships between intensive agricultural production and the environment, including the economic aspect, a subject raised infrequently in the literature. The present paper broadens the knowledge concerning the relationships between methane and nitrous oxide emissions and the economic results of agricultural holdings with different specializations. This research can serve as a basis for creating models for the development of agriculture. Research conducted until now has been on a regional scale [7,36]. Based on previous research, we can only make approximate inferences about the applied technologies in terms of their relationship with methane and nitrous oxide emissions. The advantages of the research in this paper, at the level of individual farms, are that it increases the accuracy of estimating greenhouse gas emissions and makes it possible to determine dependencies between emissions and economic results. Few analyses have raised these issues. Methods of reducing GHG emissions by changing production technology are necessary. These methods should contribute to improving environmental protection and reducing production costs. Specialized farms are and will continue to be the foundation of the country's food economy. The economic objective is the motivating factor for adopting a specialization. Therefore, a deeper understanding of environmental and economic relationships in agricultural production will make it possible to promote technological innovations leading to low emissions. Consequently, integrating all aspects of the low-emissions economy will contribute to raising the competitiveness of agricultural holdings.

This research also has practical value. It makes it possible to evaluate greenhouse gas emissions from agricultural production in a relatively simple way and to verify practices applied at the level of individual agricultural holdings and environmental protection.

#### **5. Limitations**

The authors are aware of the limitations of this analysis. One limitation is due to the choice of research subjects. The research subjects are commodity farms that are achieving higher results than average farms in Poland, which hinders the ability to generalize and make inferences. Another limitation is that the estimation of greenhouse gas emissions was done indirectly due to the lack of detailed data. There is a clear need to supplement databases with data allowing for environmental assessment of agricultural holdings. This has also been noted by other authors [29,30].

Yet another limitation is the limited number of production profiles. Further research should account for all production profiles adopted by farms and for their economic results. This is important with regard to the efficacy of methods of reducing methane and nitrous oxide emissions in agriculture.

**Author Contributions:** Conceptualization: Z.K.-C.; Methodology: L.S. and Z.K.-C.; Data curation: Z.K.-C.; Formal analysis: Z.K.-C. and L.S.; Investigation: L.S.; Z.K.-C. and R.T.; Supervision: Z.K.-C.; Writing—original draft: Z.K.-C. and L.S.; Writing—review and editing: Z.K.-C., L.S. and R.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** Publicly available datasets were analyzed in this study. The data can be found here: https://fadn.pl/publikacje/wyniki-standardowe-2/wyniki-standardowe-sredniewazone/ (accessed on 13 March 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

**Table A1.** Number of farms in 2004–2018.






**Table A2.** *Cont.*

Source: Own calculation based on FADN data [28].

**Table A3.** Eigenvalues and proportions of total variance in 15 years as explained by the first two principal components for original traits and correlation coefficients between these traits and the first three PCs on milk production farms.



**Table A3.** *Cont.*

Source: Own calculation based on FADN data [28].

**Table A4.** Eigenvalues and proportions of total variance in 15 years as explained by the first three principal components for original traits and correlation coefficients between these traits and the first three PCs on field crop farms.



**Table A4.** *Cont.*

Rotation Method: Varimax with Kaiser Normalization.

Source: Own calculation based on FADN data [28].

**Table A5.** Eigenvalues and proportions of total variance in 15 years as explained by the first four principal components for original traits and correlation coefficients between these traits and the first four PCs on mixed production farms.



**Table A5.** *Cont.*

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Source: Own calculation based on FADN data [28].




Source: Own calculation based on FADN data [28].

*Energies* **2021**, *14*, 4904


**Table**



