A Comprehensive Methodology for Identifying Cadastral Plots Suitable for the Construction of Photovoltaic Farms: The Energy Transformation of the Częstochowa Poviat

Abstract

In the era of growing energy demand and the need to reduce greenhouse gas emissions, the development of renewable energy sources, including photovoltaic farms, is becoming a key element of a sustainable energy transition. In this context, the careful selection of cadastral plots on which farms can be built is crucial, as appropriate location influences the investment’s energy efficiency and minimizes environmental and planning risks. This article presents a proprietary methodology for identifying cadastral plots that are suitable for locating a photovoltaic farm. The presented methodology integrates the Fuzzy-AHP multi-criteria analysis method and the Fuzzy Membership fuzzy logic method, thereby reducing the subjectivity of expert assessments and improving the accuracy of estimating the values of factors considered in the research. A key element of the methodology is a detailed analysis of land and building register data, which results in the identification of specific plots with high investment potential. The multi-criteria analysis considered eight key factors related to climate, terrain, land cover, and cadastral data. Based on this, eight plots and 32 plot complexes were selected as the most suitable for the construction of PV farms. The most favorable locations were identified primarily in the eastern part of Częstochowa Poviat, as well as in the northern municipalities. The proposed methodology provides a ready-to-use, practical solution to the investment challenge of selecting specific cadastral plots for new solar investments. According to the reviewed literature, each of the 40 designated sites could support a photovoltaic farm of an estimated capacity of at least 1 MW. The obtained results provide significant input into the renewable energy investment planning process and emphasize that careful selection of plot locations is crucial for the investment’s success and the region’s sustainable energy transformation.

1. Introduction

In Poland, electricity demand has been increasing year by year across various sectors of the economy. Currently, the energy sector still relies heavily on non-renewable fuels. Their combustion negatively impacts the environment [1]. In addition to CO₂ emissions, which significantly contribute to climate change, the combustion of fossil fuels produces highly harmful substances such as dust, ash, sulfur and nitrogen oxides, heavy metals, and radioactive isotopes. Furthermore, fossil fuel resources, particularly those readily available, are gradually depleting, leading to increased extraction costs and, consequently, rising electricity prices [1,2]. The transition from fossil fuels to renewable energy sources in Poland constitutes a key component of the country’s energy transition, which aims to reduce greenhouse gas emissions, enhance energy security, and promote sustainable development [3,4,5,6,7,8,9]. Renewable energy, by diversifying the energy mix, plays a crucial role in achieving both national and European climate and energy objectives. Among renewable sources, electricity generation using photovoltaic panels is considered one of the most promising sectors. Compared to other renewables, photovoltaic installations carry a lower risk of environmental damage and entail lower operation and maintenance costs. Unlike wind farms, photovoltaic installations do not generate noise nuisance [9,10,11]. Investments in renewable energy, particularly in photovoltaic farms, are therefore not only a response to growing electricity demand but also a strategic element of Poland’s broader energy transition, supporting a more sustainable and resilient energy system.

1.1. Current State of Development of Photovoltaics in Poland

Photovoltaics remains the clear leader among all other renewable energy sources in Poland in terms of development rate and installed capacity. Currently, PV capacity accounts for approximately 60% of the installed capacity in the entire renewable energy sector. Compared to other European Union countries, Poland ranked fourth in terms of PV capacity growth in 2023 and rose to the sixth place in terms of cumulative installed capacity [12].

The dynamic development of photovoltaics in Poland is due to favorable legal regulations. Changes in the regulatory environment that were introduced in 2023/2024 support the development of renewable energy sources in Poland and the European Union. The most important national legal include are the amendments to the Energy Law and the Renewable Energy Sources Act that implement the provisions of the EU RED II directive, which foresees an increase in the share of renewable energy in total energy consumption to at least 32%. In October 2023, the European Parliament and the Council of the European Union adopted a new directive—RED III, setting a target of 42.5%, with the aim of achieving 45% by 2030. Among the new legal acts that will be important for the industry, it is impossible not to mention the Net-Zero Industry Act adopted by the European Parliament in April 2024. This regulation assumes that the EU’s production capacity for strategic carbon-neutral technologies, including photovoltaics, will reach at least 40% of annual needs by 2030. The latest sectoral strategic document, adopted on 15 April 2024, is the European Solar Charter. It represents the highest level of consensus between the European Commission, member state governments, and the PV industry (investors, installers, and equipment manufacturers) for cooperation towards further comprehensive and dynamic development of photovoltaics in the EU. The Government of the Republic of Poland is a signatory to the Charter [12].

According to the results of the analyses published by Statistics Poland, the share of renewable energy in primary energy (energy contained in primary energy carriers obtained directly from renewable and non-renewable natural resources) in Poland in 2023 was 24.5%. The share of photovoltaics in renewable energy sources was almost 8% [13].

An analysis of the dynamics of the photovoltaic market in 2023/2024 indicates a slowdown in growth in the group of micro-installations (with a total installed capacity not exceeding 50 kW, i.e., so-called prosumer installations), with a simultaneous increase in installed capacity in small installations (with a capacity range of 50–1000 kW) and farms exceeding 1 MW [12]. The figures below present the current number of photovoltaic installations and their total installed capacity for each voivodship (Polish administrative region) in Poland as of 30 March 2025. Figure 1 shows only photovoltaic farms, while Figure 2 includes both farms and small photovoltaic installations.

According to the maps presented above, the smallest number of small photovoltaic installations and farms is found in southeastern Poland. The regions with the lowest total installed capacity correspond to these areas. The fewest photovoltaic farms (with installed capacity over 1 MW) are located in the Opolskie, Śląskie, Małopolskie, and Świętokrzyskie voivodships. Considering both farms and small installations, the lowest values are in Opolskie and Małopolskie, with slightly higher values in Śląskie and Pomorskie. The leader in both number and total capacity of photovoltaic installations is the Wielkopolskie voivodship.

In line with European guidelines, which call for an increased share of renewable energy in total energy production, the share of renewable energy in total electricity generation in Poland was analyzed for each voivodship. Additionally, the efficiency of renewable energy production relative to local electricity demand was examined for each voivodship (Figure 3). The data presented in Figure 3 refer to the year 2023 [13].

Figure 3 indicates that the lowest share of renewable energy in total energy production, below 29%, is found in southern and central Poland’s voivodships. For most of these voivodships, except for the Świętokrzyskie Voivodship, the share of renewable energy in the voivodship’s total energy consumption per capita is below 25%. Northern Poland leads in green energy production. According to statistical data from 2023, the Zachodniopomorskie Voivodship achieves a surplus of renewable energy production per capita.

Statistical values indicating social acceptance of photovoltaic investments and their profitability and the need to meet EU climate and energy security goals, predict a continued growth in the number of such installations in Poland [12,13,15,16]. Furthermore, Poland has significant, yet still untapped, potential in solar energy. In Germany, which is located at similar latitudes, the total installed capacity of photovoltaics at the end of 2023 was more than four times higher than in Poland [17]. Investments in this type of installations will enable an increase in the share of renewable energy in the energy mix.

1.2. Brief Overview of the Methods and Factors Used for Determining the Location of Solar Farms

Selecting a photovoltaic farm location is a complex process based on multiple environmental, technical, economic, and legal factors. This necessitates the use of multi-criteria decision-making models [8]. MCDM allows for the consideration of all necessary criteria (provided that appropriate data exist) and decision-making based on priority elements. This method also enables quantifying criteria in relation to their importance relative to other decision-making factors [18]. The spatial nature of determining the location of a potential photovoltaic farm requires the integration of multi-criteria models with GIS tools. Many researchers have utilized GIS tools to support the site selection analyses of photovoltaic farms, allowing for efficient processing and visualization of spatial data [8,9,10]. Based on the literature review conducted by the authors [19,20,21,22,23,24,25,26,27], it is estimated that the AHP (Analytic Hierarchy Process) method has been the most widely used multi-criteria decision-making method in the process of determining the location of photovoltaic farms. This method was proposed by the American mathematician Thomas L. Saaty, professor at the University of Pittsburgh [28]. AHP involves presenting a decision problem in the form of a hierarchical structure, with the decision objective at the top, the criteria influencing it below, and decision variants at the bottom [29]. The constructed hierarchical model allows for the collection of all factors influencing the decision objective in one place. The next step in this method is an analysis, which involves decision-makers comparing all pairs of factors and determining the relationship between these factors. It uses the so-called bipolar, fundamental nine-point scale. The weighting coefficients of the defined criteria are then calculated. The final step is quality analysis, i.e., the verification of consistency (logicality) reflecting the reliability and competence of experts [29]. The AHP methodology has been recognized by the international scientific community as a robust and flexible multi-criteria decision-making tool for analyzing complex decision-making problems [30]. However, the AHP method also has certain drawbacks. The main one is the lack of consideration of uncertainty that results from the quantification of decision-makers’ opinions, which can significantly affect the final analysis result. To minimize the subjectivity of the assessment, the so-called Fuzzy expert opinions, which allow for better representation of ambiguity in the decision-making process [31,32,33,34,35], are included. Fuzzy expert opinions were included in the F-AHP (Fuzzy Analytic Hierarchy Process) method, which has also been applied in the process of locating photovoltaic farms [8,36,37,38,39,40]. Other multi-criteria assessment methods less frequently used in determining areas for photovoltaic farms include, for example, TOPSIS [41,42], PROMETEE [43], and ELECTRE [44]. Sometimes these methods are combined with AHP [45,46,47]. In such hybrid combinations, the role of AHP or F-AHP is to determine the weight values of the criteria, while methods such as TOPSIS or PROMETEE are used to compare and evaluate the alternatives. Machine learning in the form of Random Forest, Multi-Layer Perception, and XGBoost algorithms has relatively recently started to be used in the field [48,49].

The publications cited above utilized various factors to determine the location of a photovoltaic farm. The final set of criteria was selected based on the nature of the study area, the availability of spatial data, suggestions based on the literature review, and the opinions of the experts involved in the study. In Ref. [25], a study examining the suitability of sites on the Philippine island of Sibuyan utilized a total of 15 factors related to climate, distance, terrain and land cover, weather, and land susceptibility to disasters. In the category of climatic factors, the highest weight was assigned to Diffuse Horizontal Irradiation (DHI), followed by Temperature (T), Global Horizontal Irradiation (GHI), Direct Normal Irradiation (DNI), and finally Solar Photovoltaic Power Output (SPVPO). In terms of location, the most important factor was considered to be the distance from coastal areas. It was followed by distance from roads, and finally from transmission lines. In the third category, the highest weight was assigned to altitude, then slope, and finally land cover. Relative humidity was considered more important than mean annual cloud cover and flood susceptibility was considered more important in relation to landslide susceptibility. In the study [26] on the Greek island of Rhodes, factors such as distance from residential areas, distance from airports and archaeological sites were additionally taken into account. Other factors that were considered when designating sites for large-scale photovoltaic installations included: tall vegetation [50], surface water [50], site exposure [50,51], wind speed [52], precipitation of rain, snow, sleet, or hail [52], atmospheric pressure [52], dusty days [53], distance from wetlands [53], wildlife and endangered species [45], emission of harmful pollutants [45], soil class [45,51], and visual aspect [45]. Furthermore, some studies also considered certain economic and social factors. The economic factors include electricity demand, construction costs, operation and maintenance costs, and electricity transmission costs [52]. Distance from road infrastructure and distance from transmission lines are also considered in the context of economic factors [45]. Criteria defined as social, which have been included in publications on determining the location of photovoltaic farms, were also used. They include quality of life of residents [45,52], support mechanisms [52], social regulatory compliance [45,52], government policies and laws [52], impact on agriculture, employment, and tourism [45], impact on the economic development of the region [45], and distance from residential buildings [45]. Studies [54] additionally considered the plot area, while [20,55] also considered the shape and perimeter of the plots. Most of the reviewed studies considered climatic factors, land cover, and terrain-related factors. Social, economic, and accounting aspects were considered less frequently.

1.3. Purpose of the Research

The aim of the research presented here is to develop a proprietary method for determining the most favorable cadastral plots for locating photovoltaic farms using integrated spatial data, GIS tools, and multi-criteria analysis. The proposed methodology extends approaches reported in the literature, delivering a highly practical outcome. The methodology integrates key factors identified in international studies, including climate, land cover and topography, and land records, and analyzes them comprehensively. Its novelty lies in the combined use of the Fuzzy-AHP method (instead of the classic AHP) and the Fuzzy Membership method to demonstrate the gradation of suitability of specific areas for each factor. This approach reduces subjectivity in assessments and improves the accuracy of land suitability estimates through fuzzy logic. Furthermore, the methodology incorporates an in-depth analysis of land records, which has not been conducted to this extent before. As a result, the study produces a ready-to-use list of cadastral plots with high potential for solar farm development. The detailed research was preceded by a nationwide analysis, allowing the authors to narrow the study area to regions with the greatest potential for renewable energy development. Based on this methodology, the research seeks to answer the following questions:

• Which areas exhibit the highest potential for the location of photovoltaic farms, considering climatic factors, topography, land cover, and cadastral status?
• How can the integration of cadastral data with multi-criteria analysis support the planning process of PV investments at the local level?
• Which multi-criteria analysis methods can reduce the impact of uncertainty and expert subjectivity in the assessment of photovoltaic farm locations?

2. Materials and Methods

2.1. Selection of Research Area

The research began with an analysis of the entire territory of Poland in order to determine a suitable area for further, detailed research on the location of solar farms. This preliminary analysis, in addition to the maps developed in Section 1.1, took into consideration three additional factors: solar irradiation, air pollution levels, and the presence of existing large photovoltaic installations.

The figures below present visualizations of sunshine duration as the most important factor in locating photovoltaic farms. The term “sunshine duration” refers to the number of hours in a given period during which direct solar irradiation reached the Earth’s surface at a given point [13]. Figure 4 shows the average sunshine duration in Poland from 1991 to 2000, while Figure 5 shows the recorded values for this factor in 2024. Figure 4 and Figure 5 are studies by the Institute of Meteorology and Water Management [56].

Figure 6 also presents the locations of photovoltaic farms (installations exceeding 1 MW) in Poland, as of 31 March 2025. The farm locations are approximate, based on the National Register of Geographical Names, which contains points representing the locations of towns and cities in Poland.

During the 30-year measurement period (Figure 4), average sunshine duration in Poland ranged from 1550 to 1900 h. In 2024 it ranged from 1700 to 2300 h, with most of the country falling within the 1800–2200 h range. During the long-term period under review, the highest average sunshine duration values were recorded in western and eastern Poland, with relatively high values observed in the central part of the country. In 2024, the highest values were observed in the eastern, central, and southeastern parts of the country. The distribution of existing photovoltaic farms is quite uneven, with a predominance of such installations in western, northern, and southeastern Poland, and a deficit in southern and central voivodships. Two indicators of gaseous pollutant emissions from particularly noxious plants were used to analyze air quality in individual voivodships: total pollutant emissions [tons/km²] (Figure 7) and carbon dioxide emissions [tons × 10⁶/year] (Figure 8) [13].

The highest air pollution levels, based on the two indicators cited, are recorded in the Śląskie and Łódzkie Voivodships. Despite a visible decline in these indicators over the years 2020–2024, their values for the Śląskie and Łódzkie Voivodships remain relatively high.

Given the need to simultaneously identify an area with relatively high sunshine duration, a lack of investment in photovoltaic farms, and a need for air quality improvement, the study area was selected. It is the Częstochowa Poviat, located in the northern part of the Śląskie Voivodship. This area is marked with a red rectangle in Figure 4, Figure 5 and Figure 6. Sunshine duration in this area fluctuated above the average observed values. To date, no photovoltaic farms (with an installed capacity exceeding 1 MW) have been built there. According to the list published by the Energy Regulatory Office, as of 31 March 2025, there are ten small photovoltaic installations in the Częstochowa Poviat with the installed capacity ranging from 0.199 MW to 1 MW (Figure 9).

The Śląskie Voivodship, of which Częstochowa Poviat is a part, also experiences high air pollution compared to other Polish voivodships. This is largely due to the high consumption of conventional fuels within the region. In 2023, hard coal consumption in the Śląskie Voivodship was the highest in the country, amounting to 25.3% [13]. Furthermore, as shown in Figure 1, Figure 2 and Figure 3, the voivodship requires additional investments in renewable energy sources. Increasing the number of efficient photovoltaic installations, which can replace traditional energy sources, is therefore crucial in the attempts to improve air quality in the region.

2.2. Methodology

The research was divided into three main stages (Figure 10), each being a logical extension of the previous one. At the first stage, based on an extensive literature review, key criteria influencing the location of photovoltaic farms were identified, including climatic conditions, land cover and topography, and information from the land register [57,58]. In parallel, so-called elimination factors, i.e., factors that preclude the implementation of the investment, were identified. The Boolean method [20] allowed for the rejection of areas that did not meet the basic requirements. After that, spatial data was prepared and standardized for further analysis.

The second stage of the research resulted in the development of a site suitability map for the location of photovoltaic farms. For this purpose, the Fuzzy-AHP method was used to determine the weights of individual criteria, as well as elements of fuzzy logic (Fuzzy Membership method). They enabled the transformation of input values onto a continuous scale (0–1) and thus captured the gradation of the suitability of individual areas. Combining the results using the WLC (Weighted Linear Combination) procedure [59] and eliminating areas with a lack of real investment opportunities allowed the authors to obtain a reliable map of investment potential.

The final stage focused on the detailed level—an analysis of cadastral plots located in the highest-rated areas. This made it possible to identify not only theoretically advantageous areas but also those that were practically available for future investment.

2.2.1. Factor Selection and Data Preparation

In this study, the authors considered the most important factors that influence the physical feasibility of constructing a solar farm. According to the authors, environmental conditions and land records play a primary role. If appropriate climatic conditions, land cover, and topography are not present, the construction of a solar farm is not possible. In this context, other factors, such as social and economic aspects, play a secondary role.

Eight factors were identified as determinative for the location of a large photovoltaic installation, two were eliminative (physically preventing the construction of the farm), and four were related to land records.

The factors selected were: insolation, distance from major roads, distance from medium-voltage power lines, terrain exposure, slope, soil class, average monthly precipitation, and distance from residential buildings.

A key factor in selecting a location for a photovoltaic farm is solar irradiation, which largely determines the installation’s efficiency. The higher the insolation, the higher the farm’s efficiency [25,37]. Insolation values were calculated based on a Digital Terrain Model (DTM) downloaded with a spatial resolution of 5 m from the central node of the Spatial Information Infrastructure in Poland [60]. Other factors considered and determined based on the DTM were exposure and slope. The site for a photovoltaic farm should be flat or south-facing [61]. According to [25], the slope of the terrain should not exceed 4% to avoid mutual shading of the solar panels.

The numerical terrain model used to calculate the insolation, slope, and exposure values of the terrain was first downscaled to a spatial resolution of 50 m in order to speed up the calculations while maintaining the required level of detail. Insolation values for 2024 were determined (Figure 11). The slope (Figure 12) and exposure (Figure 13) were then calculated.

Proximity to a network of paved roads plays a significant role in minimizing the costs of both the farm’s construction and maintenance [25,55,61]. To ensure the economic viability of the investment, the selected location should have access to a medium-voltage power line, preferably within 200 m. The greater the distance, the higher the connection costs [55,61]. Exceeding the 500 m distance results in additional costs at the planning stage and prolongs the process of obtaining a building permit [61]. Given the similar solar irradiation conditions throughout the study area, according to [26], distance from roads and power lines are the most important factors determining the location of a photovoltaic farm. Although under Polish law, photovoltaic farms may be constructed near residential areas, investors tend to avoid these locations due to the need to obtain additional environmental impact assessments and the risk of local protests [61]. Studies have assumed that the further away from built-up areas, the better the conditions for building a photovoltaic farm. Data regarding the location of road networks, power lines, and residential buildings was obtained from a spatial database with a level of detail equivalent to a 1:10,000 topographic map—BDOT10k (Database of Topographic Objects) [60]. Roadway vectors (SKJZ code in BDOT10k) were filtered to include only paved roads in the study. Roadways that were not connected to any other roadway and therefore did not provide continuity of traffic were removed. A buffer was then generated to eliminate the possibility of locating structures around the roadway. This buffer comprised the roadway width and the distance required for the location of structures around the roadway in accordance with the Public Roads Act [62]. At the end of the data preparation stage, a raster containing the Euclidean distance values in meters from the buffers was generated (Figure 14). From the vector file containing the location of power lines (SULN code) in the poviat, the vectors representing medium voltage lines were selected. Then the Euclidean distance from them was also calculated (Figure 15).

Residential buildings were selected from the file (BUBD code), and a buffer was designated for them, defining the area that could be shaded by these structures. The distance from this buffer was then presented in raster format within the poviat boundaries. The width of the zone limiting the possibility of establishing a farm around the buildings was estimated based on the maximum height of residential buildings of 10 m and the angle of sunlight at 12:00 on the summer and winter solstices, and the spring and autumn equinoxes at the latitudes of Częstochowa. The value of the permissible height of residential buildings was adopted from the local spatial development plan for Czarny Las, located in the northern part of Częstochowa Poviat [63] (Figure 16).

Another important factor is soil class. In Poland, soil classes IV-VI are most commonly chosen for photovoltaic installations. A farm may also be located on higher-quality soils, but the investment process in such cases is more complex and expensive [61]. The vector valuation data used in this study was acquired from the land and building register maintained by the Poviat Center for Geodetic and Cartographic Documentation in Częstochowa.

Data regarding land use and valuation classes was divided into four groups: a value of “1” was assigned to classes I to III and land that prevent the construction of a farm, e.g., land designated with the abbreviation Ls for forests or Br for developed agricultural land; a value of “2” to valuation classes IVa and IVb; “3” was assigned to class V; and a value of “4” to class IV. Values from “2” to “4” include meadows (marked with the symbol “Ł”), arable land (“R”), and permanent pastures (“Ps”). As the “value” of a class increases, its usefulness for establishing a farm also increases, and it is assumed that class “1” is not useful (Figure 17).

Precipitation affects the productivity of a photovoltaic farm. Rain and snow can obstruct the path of solar radiation to the panels, thus reducing electricity generation efficiency [64]. The data used in this study regarding monthly precipitation totals at precipitation stations in the study area was obtained from the repository of the Institute of Meteorology and Water Management (IMGW) [65]. The air humidity factor [66] and the number of rainy/snowy days [64] could not be included due to the lack of appropriate measurement data.

The data regarding monthly precipitation totals at precipitation stations in the study area was interpolated using the Natural Neighbor method [67]. According to Figure 18, the average monthly values in 2024 were in the range of 21–27 mm.

The following categories of objects were identified as elimination factors, selected based on land cover data from BDOT10k [60]: water network, communication network, pipelines, surface water, development, forest or wooded area, shrub vegetation, permanent crops, communication area, square, landfill, excavation and dumping ground, other undeveloped land, buildings, structures and facilities, land use complexes, protected areas, and other objects. In addition to the selected BDOT10k data, floodplains were also identified as preventing the construction of photovoltaic farms. Data was collected from the Initial Flood Risk Assessment (PFRA) in vector format, provided by State Water Holding—Polish Waters as part of the ISOK project [68]. A buffer zone was designated for the selected data identified as elimination factors. A buffer of 6 m was considered for pipelines [69]. Due to the lack of differentiation between individual trees and groups of trees in the BDOT10k data, the same buffer was used for both forested and wooded areas and smaller such facilities. A buffer of 40 m was included to minimize the impact of tree shading. This value was calculated based on the estimated average tree height of 20 m and the angle of sunlight at 12:00 on the summer and winter solstices and the spring and autumn equinoxes at the latitudes of Częstochowa. A buffer of 20 m was used for buildings. The average building height used in the calculations was derived from data provided in the Land and Building Register. Based on these input parameters, the estimated shadow lengths for trees were approximately 12 m, 75 m, 43 m, and 35 m, respectively, while for buildings they were approximately 6 m, 37 m, 21 m, and 17 m. The final buffer value was determined as the arithmetic mean of these four characteristic shadow lengths, representing the average seasonal shading effect. The surfaces of all roadways were also eliminated. For the airport, a buffer of 2 km [26,50] was used. The extent of the eliminated areas is presented in Figure 19.

2.2.2. Raster Transformation Using the Fuzzy Membership Method and Determining the Criteria Weights Using the F-AHP Method

To determine the suitability of the terrain for individual factors, the prepared raster data was classified. There are two approaches in this regard: classical and fuzzy modeling. The classical model is based on two-valued logic; elements that meet the assumptions receive a value of “1” and those that do not are assigned the value of “0”. This study was based on fuzzy classification, using the Fuzzy Membership method, to account for intermediate values (between the extreme values of “0” and “1”), indicating partial suitability of individual criteria. So-called membership functions used for this purpose can take various forms, e.g., continuous/discontinuous, linear/nonlinear, or symmetric/asymmetric [70,71]. Membership functions for the prepared raster factors were applied according to the type described in

Table 1. Fuzzification of factors.

Factor	Values	Function Type
Solar irradiation [kWh/m2/year]	490,730–1,099,248	Linear increasing
Slope [%]	0–4	Linear decreasing
Aspect	(1) S and flat (2) SE and SW (3) E and W (4) N, NE and NW	Linear decreasing
Distance from roads [m]	0–3747	Linear decreasing
Distance from power lines [m]	0–3908	Sigmoidal decreasing, Midpoint: 400 m, Spread: 3
Distance from residential buildings	0–3529	Linear increasing
Land use and soil class from Land and Building Register	(1) unsuitable land use and I-III soil classes (2) Ł/R/Ps, IVa-IVb (3) Ł/R/Ps, V (4) Ł/R/Ps, VI	Linear increasing
Average monthly precipitation [mm]	21–27	Linear decreasing

.

Based on the literature review, expert knowledge, and the characteristics of the study area, two types of membership functions were adopted. A linear function (increasing or decreasing) was used for 7 of the 8 factors, assuming that the site’s suitability directly increases or decreases with the factor value. A sigmoidal function was selected for the “distance to power lines” criterion. The decreasing sigmoidal function selected in this case allows for increasing the importance of areas located within 500 m of medium-voltage lines relative to other areas, while simultaneously assuming that areas within 200 m are ideal (close to or equal to “1”). As a result of applying the selected functions, eight rasters were obtained with values ranging from 0 to 1 (Figure 20). The value “0” indicates areas that are completely unsuitable for a photovoltaic farm, while “1” indicates the most suitable areas.

To determine the criteria weights, the Fuzzy Analytic Hierarchy Process (F-AHP) method was used. This method is an extension of the classical Analytic Hierarchy Process (AHP) method proposed by Saaty (1980) by introducing fuzzy logic to capture the uncertainty and subjectivity of expert judgments [28]. In the classical AHP, pairwise comparisons of criteria are made on a rigid numerical scale (1–9), which forces unambiguous indications of preferences, which in reality may be fuzzy. F-AHP solves this problem by using triangular fuzzy numbers (TFN), which are defined as a triplet (l, m, u), where l is the lower bound, m the most probable value and u the upper bound. The membership function ${\mu }_{\tilde{A}}\left(x\right)$ for the fuzzy number M = (l, m, u) is described by the formula:

$${\mu }_{\tilde{A}}\left(x\right) = \left\{\begin{array}{c}{\displaystyle \frac{x - l}{m - l}} , l\le x\le m\hfill \\ {\displaystyle \frac{u - x}{u - m}} , m\le x\le u\hfill \\ 0 , otherwise\hfill \end{array}\right.$$

(1)

where

l—lower limit of the interval,

m—most probable value,

u—upper limit of the interval

The F-AHP begins with the construction of a hierarchy of the decision problem, which includes the main objective, main criteria, and any sub-criteria. Experts then perform pairwise comparisons of criteria, assigning fuzzy numbers that describe the importance of one criterion relative to another. All these evaluations create a fuzzy comparison matrix, which is then aggregated to obtain a synthetic degree of importance for each criterion. One popular aggregation method is the Extent Analysis Method, proposed by Chang (1996), which allows for the calculation of the so-called fuzzy degree S_i for each criterion [72].

$$S_i={\displaystyle \sum _{j=1}^m}\widetilde{M_{ij} }{\otimes \left(\sum _{i=1}^n\sum _{j=1}^m\widetilde{M_{ij}}\right)}^{-1}$$

(2)

where

S_i—fuzzy synthetic extent for the i-th criterion,

($\widetilde{M_{ij}}$)—triangular fuzzy number (TFN) for the j-th comparison of the i-th criterion,

i = 1,2,…,n—index of criteria,

j = 1,2,…,m—index of comparisons (extents) for criterion

n—total number of criteria,

m—number of extents for each criterion,

⊗—multiplication of fuzzy numbers,

After obtaining fuzzy importance levels, it is necessary to defuzzify them, i.e., convert the fuzzy numbers into scalar values that can be directly used in further analyses. The most commonly used defuzzification methods are the Centroid Method, which was used in this analysis, Mean of Maximum (MOM) and Mid Range Method. The resulting weights are then used in multi-criteria analyses, e.g., in the Weighted Linear Combination (WLC) method for generating land suitability maps. To assess the consistency of the comparison matrix, the consistency index (CI) and consistency ratio (CR) proposed by Saaty [28] are typically used.

2.2.3. Land Register Analysis

The land register in the areas considered most suitable for building a solar farm was analyzed. The geometric and ownership criteria taken into account were as follows: plot area, plot width, plot shape, and the number of plots. Regarding the plot area, assumptions were made in line with the guidelines for conditions prevailing in Poland [61]—a plot for a 1 MW solar farm should cover approximately 1.5 [ha] and be at least 50 m wide. The plot shape index W_S was used to assess the plots, according to Equation (3) [73]:

$$W_s = {\displaystyle \frac{P}{P_0}}, \mathrm{where} P_0= 1.5·{\left({\displaystyle \frac{O}{5}}\right)}^2$$

(3)

In Equation (3), P is the plot area, P₀ is the optimal plot area, and O is the plot perimeter.

The above indicator was chosen because its reference point is a rectangle with a 3:2 aspect ratio, not a circle [74]. This indicator takes values in the range (0—1.326>), with a value of “1” for a rectangle with a 3:2 aspect ratio, a square with a value of “1.042”, and a circle with a value of “1.326”. However, for an infinitely long geometric figure, the indicator tends to “0”.

The last feature of the land register taken into account is the number of plots for purchase. To minimize the time associated with the purchase/lease of cadastral plots for farms, it was assumed that the number of plots for investment should be as small as possible [20].

3. Results

3.1. Determination of Weights

The weights of the selected criteria for the farm location analysis were determined based on Fuzzy-AHP hierarchical decision analysis. Factors were compared pairwise using a dominance scale, where the highest value indicated complete dominance of one criterion over the other, and the lowest value indicated no dominance. Eight detailed criteria were used to select the location: solar irradiation, distance from roads, distance from power lines, terrain exposure, slope, land use and soil class from the Land and Building Register, average monthly precipitation, and distance from residential development.

Each of the five experts in geoinformatics and spatial planning who participated in the study, independently completed pairwise comparisons for all criteria. Experts completed a triangular fuzzy judgment matrix, and the average rating was used to calculate the weights of individual criteria. The resulting weights are presented in

Table 2. Results of Fuzzy-AHP method; the weight values and ranking number are in bold; own study.

	Solar Irradiation	Slope	Distance from Power Lines	Aspect	Land Use and Soil Class	Distance from Roads	Average Monthly Precipitation [mm]	Distance from Residential Buildings
Solar Irradiation	(1; 1; 1)	(2.67; 3.67; 4.67)	(0.78; 0.83; 1)	(1; 1; 1)	(1; 2; 3)	(2; 3; 4)	(3.33; 4.33; 5.33)	(4; 5; 6)
Slope	(0.22; 0.28; 0.39)	(1; 1; 1)	(0.25; 0.33; 0.50)	(0.33; 0.50; 1)	(1; 2; 3)	(0.33; 1.33; 2.33)	(2; 3; 4)	(2; 3; 4)
Distance from Power Lines	(1; 1.33; 1.67)	(2; 3; 4)	(1; 1; 1)	(1; 1; 1)	(1; 2; 3)	(1.33; 2.33; 3.33)	(2.33; 3.33; 4.33)	(4; 5; 6)
Aspect	(1; 1; 1)	(1; 2; 3)	(1; 1; 1)	(1; 1; 1)	(1; 1.33; 1.67)	(1; 1; 1)	(4; 5; 6)	(5.67; 6.67; 7.67)
Land Use and Soil Class	(0.33; 0.50; 1)	(0.33; 0.50; 1)	(0.33; 0.50; 1)	(0.78; 0.83; 1)	(1; 1; 1)	(0.56; 0.67; 1)	(2; 3; 4)	(2.67; 3.67; 4.67)
Distance from Roads	(0.25; 0.33; 0.50)	(1; 2; 3)	(0.31; 0.44; 0.83)	(1; 1; 1)	(1.00; 1.67; 2.33)	(1; 1; 1)	(3.33; 4.33; 5.33)	(4; 5; 6)
Average Monthly Precipitation [mm]	(0.19; 0.23; 0.31)	(0.25; 0.33; 0.50)	(0.23; 0.31; 0.44)	(0.17; 0.21; 0.26)	(0.25; 0.33; 0.50)	(0.19; 0.23; 0.31)	(1; 1; 1)	(4; 5; 6)
Distance from Residential Buildings	(0.17; 0.20; 0.25)	(0.25; 0.33; 0.50)	(0.17; 0.20; 0.25)	(0.13; 0.15; 0.18)	(0.22; 0.28; 0.39)	(0.17; 0.20; 0.25)	(0.17; 0.20; 0.25)	(1; 1; 1)
WEIGHTS	0.2098	0.0969	0.2035	0.1608	0.1066	0.1380	0.0475	0.0269
RANK	1	6	2	3	5	4	7	8

. The analysis revealed that factors related to solar irradiation (21%) and distance from power lines (20%) were the most significant, while distance from residential development (3%) was assigned the lowest weight. Environmental factors such as sunlight, exposure, and slope received significant weighting, indicating the importance of natural conditions in locating the study areas. In turn, the weighting of infrastructure criteria (distance from roads and power lines) emphasizes the role of accessibility and safety. Land use and soil class received moderate weighting (11%), while average precipitation had relatively little significance (5%). The calculated fuzzy aggregated decision matrix of criteria, along with the normalized priority weights, is presented in Table 2. This figure summarizes the outcomes of the pairwise comparison process carried out with the Fuzzy-AHP method and illustrates the relative importance of each criterion after normalization.

The consistency of the pairwise comparisons was then verified using the consistency index (CI) and consistency coefficient (CR). In cases of significant discrepancies, experts were consulted again to clarify their reasoning and adjust the assessments where justified. The final aggregated fuzzy weights represent a consensus derived from this structured process. The maximum eigenvalue of the comparison matrix for the eight criteria was approximately 8.70. The calculated consistency index values were CI = 0.10 and CR = 0.07, which is below the acceptable threshold (CR < 0.10). This result confirms the correctness of the expert assessments and also indicates that the assigned weights are reliable and not subject to random inconsistencies.

3.2. Development of a Suitability Map

The suitability map was developed by applying the WLC method [59] and then eliminating areas deemed physically impossible to develop into farms. As a result of weighted summation of all factors represented by raster layers, using the weight values determined in the previous step, a raster containing values in the range <0.17, 0.91> was generated with a spatial resolution of 50 m × 50 m (Figure 21). A higher pixel value in the image indicates a higher degree of land suitability for locating new photovoltaic farms. The numerical values of the generated image were divided into five equal intervals, and their suitability levels were determined as follows: <0.17, 0.31>—unsuitable, <0.32, 0.46>—less suitable, <0.47, 0.61>—moderately suitable, <0.62, 0.76>—more suitable, and <0.77, 0.91>—highly suitable. The vast majority of the study area (73%) was deemed completely unsuitable for this purpose, while 4% of the area demonstrated a low degree of suitability, and 13% a moderate degree of suitability. 9% (14,008 ha) was considered good location, and 1% (1922 ha) was considered the best. The unsuitability of the study areas was primarily due to the presence of large forests, wooded areas, flood zones, and the presence of an airport. The largest areas of highest suitability are located in the eastern part of the Poviat. Most of the areas classified as highly suitable exhibit characteristics such as solar irradiation exceeding 950,000 Wh/m²/year, terrain slope not exceeding 2°, proximity to power lines within 500 m, location on flat terrain or with southern or south-western exposure, occurrence on soils of classes IV–IVa, and distance from roads not greater than 500 m. The criteria related to the distance from residential buildings and precipitation values did not have a significant impact on the results.

The largest number of areas with the highest suitability are located in the eastern part of the study area.

Table 3. Summary of the areas of suitability classes and their percentage share in the area of municipalities in the Częstochowa Poviat; total areas for the “highly” and “more” suitable zones are in bold; own study.

Municipality Name	Highly Suitable	More Suitable	Highly and More Suitable	Moderately Suitable	Less Suitable	Unsuitable
Dąbrowa Zielona	257 ha 3%	1626 ha 16%	1883 ha 19%	1668 ha 17%	393 ha 4%	5981 ha 60%
Konopiska	27 ha 0%	354 ha 5%	380 ha 5%	297 ha 4%	49 ha 1%	7044 ha 90%
Kruszyna	184 ha 2%	1061 ha 12%	1245 ha 14%	1425 ha 16%	394 ha 4%	6093 ha 66%
Lelów	193 ha 2%	1487 ha 12%	1680 ha 14%	2689 ha 22%	1061 ha 9%	6699 ha 55%
Mstów	62 ha 1%	814 ha 7%	876 ha 8%	1940 ha 16%	948 ha 8%	8159 ha 68%
Poczesna	38 ha 1%	472 ha 8%	511 ha 9%	654 ha 11%	83 ha 1%	4687 ha 79%
Starcza	15 ha 1%	217 ha 11%	232 ha 12%	135 ha 7%	32 ha 2%	1571 ha 79%
Kamienica Polska	21 ha 1%	192 ha 4%	214 ha 5%	233 ha 5%	107 ha 2%	4010 ha 88%
Olsztyn	14 ha 0%	244 ha 2%	258 ha 2%	509 ha 5%	123 ha 1%	9951 ha 92%
Janów	43 ha 1%	636 ha 4%	680 ha 5%	907 ha 6%	225 ha 2%	12,731 ha 87%
Kłomnice	161 ha 1%	1509 ha 10%	1670 ha 11%	1832 ha 12%	427 ha 3%	10,734 ha 74%
Koniecpol	515 ha 4%	1941 ha 13%	2456 ha 17%	1632 ha 11%	190 ha 1%	10,102 ha 71%
Blachownia	88 ha 1%	315 ha 5%	404 ha 6%	173 ha 3%	26 ha 0%	5855 ha 91%
Mykanów	169 ha 1%	1768 ha 13%	1936 ha 14%	2607 ha 19%	811 ha 6%	8595 ha 61%
Przyrów	95 ha 1%	1079 ha 14%	1174 ha 15%	1723 ha 21%	584 ha 7%	4590 ha 57%
Rędziny	43 ha 1%	311 ha 8%	354 ha 9%	454 ha 11%	83 ha 2%	3216 ha 78%

presents the area of suitability classes in [ha] and their percentage share in the area of individual municipalities in Częstochowa Poviat. Taking into account both the “highly suitable” and “more suitable” classes, the most favorable land is found in the municipalities of Dąbrowa Zielona (1883 ha)—19%, Koniecpol (2456 ha)—17%, Przyrów (1174 ha)—15%, and Kruszyna (1245 ha), Lelów (1680 ha), and Mykanów (1936 ha) with a result of 14%. In turn, the southern part of Częstochowa Poviat is characterized by the lowest suitability of land for farms. The municipalities of Konopiska (380 ha), Kamienica Polska (214 ha), Olsztyn (258 ha), and Janów (680 ha) have the smallest (no more than 5%) share of areas suitable for this type of investment.

3.3. Analysis of Cadastral Plots in Areas of the Highest Suitability

Two approaches were used in the analysis of cadastral plots. The first approach aimed to find a single plot that, in addition to being located in “more” or “highly suitable” areas, would also meet a number of cadastral criteria. The second approach sought a complex of plots meeting specific conditions.

3.3.1. Analysis of Individual Cadastral Plots

A shape index was calculated for each cadastral plot in Częstochowa Poviat. Appropriate plots were identified using the following criteria:

• The shape index is in the range <0.820, 1.042> [73];
• The plot area is not smaller than 1.5 [ha] [61];
• The width of the plot is not shorter than 50 [m] [61].

The locations of the designated cadastral plots were then compared with the “highly suitable” and “more suitable” areas, marked green on the suitability map. Ten plots were found to meet the above-mentioned conditions and to be located in the most suitable areas, either entirely (

Table 4. Summary of cadastral characteristics of the plots most suitable for solar farm construction; own study.

No.	Municipality Name	Cadastral District Code	Parcel Number	Shape Index	Total Area [ha]	Length of the Shortest Side [m]	Comments
1	Poczesna	0003	543/1	0.947	1.896	53	-
2	Poczesna	0011	308/3	1.024	2.836	142.5	partially unsuitable
3	Poczesna	0015	4231/2	0.89	13.537	117.5	partially unsuitable
4	Lelów	0001	4462	0.976	2.17	123	-
5	Lelów	0003	1586	0.958	2.344	113	-
6	Lelów	0003	1587	0.862	1.687	85	-
7	Lelów	0003	1588	0.91	1.944	96	-
8	Przyrów	0012	1913	0.961	2.379	110	partially unsuitable
9	Koniecpol	0004	907	0.836	1.837	77	-
10	Kruszyna	0006	5776	1.041	3.218	187	-

) or partially, provided that the portion of the plot area located in the most suitable areas could not be smaller than 1.5 [ha] (Figure 22). Those plots whose entire area was not located in the “highly” and “more suitable” areas were marked in Table 4 as “partially unsuitable.”

The plots listed in the table, in addition to meeting the land register requirements, are characterized by their location, where

• Solar irradiation is in the range of 951,295–1,099,248 [kWh/m²/year];
• The slope is no greater than 4% (plot 4231/2 is partially located on a 4–5% slope);
• The dominant exposure is S, SE, or SW (only plot 543/1 is E dominant);
• The soils are class IV–VI (only on plot no. 1588 was an area of up to 15% of the plot area classified as class III);
• Average monthly rainfall ranges from 21 to 27 mm;
• The straight-line distance to the road is no greater than 400 m;
• The distance to medium-voltage power lines is no greater than 109 m;
• The distance to residential buildings is no less than 120 m (only for plot 4231/2 with an area exceeding 13 ha, this distance is 15 m);
• The width of the plot is not less than 50 [m] [61].

Figure 23, Figure 24, Figure 25 and Figure 26 show examples of plots deemed suitable for building a farm, outlined in blue. Figure 23 and Figure 24 show plot no. 907; the first shows the plot against a satellite image, and the second against a suitability zone. The boundaries of the cadastral plots are shown in black. Figure 25 and Figure 26 show the location of plot 4231/2, a small portion of which was deemed unsuitable.

3.3.2. Analysis of Cadastral Plot Complexes

In the second approach, cadastral plots were selected based on the following assumptions:

• The plots are located in areas designated as “more” or “highly suitable,” and the surface of these areas cannot be smaller than 1.5 [ha];
• Single plots (not adjacent to any other plot) whose area is smaller than 1.5 [ha] are rejected;
• The area of designated plot complexes cannot be smaller than 1.5 [ha], the aspect ratio is in the range <0.82, 1.042>, and the width of the complex is no less than 50 [m].

Using the above criteria, 32 cadastral plot complexes were obtained. The distribution of the complexes is shown in Figure 27.

The most suitable complexes for the construction of a photovoltaic farm are located in the municipalities of Dąbrowa Zielona (9) and Koniecpol (9). Lelów and Kruszyna each have three such complexes, while Przyrów, Poczesna, and Kłomnice each contain two. One complex was located in the municipalities of Rędziny and Konopiska. No groups of plots meeting the previously described criteria were found in the remaining municipalities.

The designated complexes were differentiated based on the number of plots they comprise. It was determined that the fewer plots (which usually results from a smaller number of owners), the more favorable the location. The most suitable complexes consist of two plots, while the least suitable complexes consist of several dozen. Four complexes range in size from 20 to 53 plots and are located in the municipalities of Dąbrowa Zielona, Koniecpol, and Poczesna. The eleven best complexes, consisting of the fewest plots (2 to 5), are located within the municipalities of Kruszyna, Dąbrowa Zielona, Koniecpol, Rędziny, Poczesna, Przyrów, Konopiska, and Lelów.

All of the designated complexes are characterized by:

• Insolation ranging from 951,295 to 1,099,248 [kWh/m²/year];
• a slope of no more than 4% (only one complex with an area exceeding 5 ha in the Poczesna commune has a small area with a slope exceeding 4%);
• dominant exposure: S, SE, or SW;
• soil class IV, V, or VI;
• average monthly precipitation of 21–27 [mm];
• straight-line distance to a road no greater than 841 [m] (whereas for 26 of 32 complexes it is less than 400 [m]);
• distance to medium-voltage power lines no greater than 312 [m] (whereas for 31 complexes this value is less than 206 [m]);
• distance to residential buildings no less than 60 [m] (whereas for 29 complexes this distance is greater than 100 [m]).

The complexes thus designated partially overlap with the individual plots designated in the first stage. Two plots in the Lelów commune overlap with the complexes, thus ultimately determining that eight cadastral plots and 32 complexes are suitable for building a PV farm.

4. Discussion

As a result of the conducted research, an innovative, comprehensive methodology was developed for determining the location of cadastral plots suitable for the construction of a photovoltaic farm. The methodology presented in this article extends the approaches reported in the international literature [25,26,39,55] while offering invaluable practical impact. This proprietary methodology integrates the use of Fuzzy-AHP (as applied), among others, in studies (3940 focused on the siting of solar farms) to eliminate the subjectivity of the classic AHP method, fuzzy logic to more accurately present the gradation of land suitability with respect to individual factors (as implemented, for instance, by the authors of studies [22,64]) and also provides a detailed analysis of the cadastral status (with reference to studies [20,55]). The outcome of the study is a ready list of specific cadastral plots within the study area that exhibit the highest suitability for solar farms. This carries immense practical value in the investment process, accelerating decisions and minimising errors in selecting the best sites.

4.1. Advantages and Disadvantages of the Methodology

The results of the study identified the most suitable locations for photovoltaic (PV) farm development within the Częstochowa Poviat, based on a combination of climatic, topographic, land cover, and cadastral factors. The analysis showed that approximately 10% of the total area was classified as suitable for PV installation, with 1% identified as highly suitable. The most influential criteria were solar irradiation, distance to medium-voltage power lines, aspect, distance to roads, land use and soil class, and slope, while precipitation and distance to residential areas had a lower impact. The following discussion elaborates strengths and limitations of the proposed approach.

The presented methodology is universal, meaning it can certainly be applied in other parts of Poland and the world. Of course, certain limitations should be noted. The selection of factors was guided by the nature of the study area, the results of similar studies in the literature [25,40,55,63], and the availability, quality, and resolution of spatial data, while striving to minimize the number of these factors. The spatial data used must be reliable and reflect the actual field conditions. Higher-resolution spatial data enable more detailed analyses. Moreover, as the authors note [75], the large number of criteria or alternatives in the AHP method negatively impacts the consistency of the assessment matrix. The authors selected the most important criteria for the study, including climate [63], topography [47], and land cover [48], as well as land and building records [55]. When applying the presented methodology to another research area, its specific characteristics should be taken into account. For example, floodplains were eliminated in this study; in other study areas, for example, landslides may be relevant [25]. Furthermore, in highly diversified areas with significant differences in elevation, it will likely be necessary to additionally consider altitude above sea level as a determining criterion.

The presented methodology still involves a certain degree of subjectivity. Although Fuzzy-AHP reduces the subjectivity of classic AHP, it still relies on expert assessments when comparing pairs of criteria. Therefore, analysis results may vary depending on the group of experts. Furthermore, subjectivity is related to the definition of the membership function. Transforming criteria into a fuzzy form requires experience, and the use of different functions may lead to different results.

Applying the multi-criteria analysis method typically terminates research into suitable locations for photovoltaic farms, as presented in the reviewed literature [25,49,55], yet this approach does not yield actual, measurable results. Only by expanding the analysis to include legal status, in the form of land and building records, does the methodology become applicable. However, it should be noted that access to this type of data may be difficult in many regions of the world.

Eight factors determining the location of a PV system were selected. The article did not verify the technical conditions for the PV farm’s location, as demonstrated for instance by the authors of [45], due to the lack of access to the relevant data. Information regarding critical infrastructure, including the technical condition and load of power lines, is not publicly available. Pursuant to the Regulation of the Minister of Climate and Environment [76], an application must be submitted to the distribution system operator, who will then indicate the closest possible grid connection point for the farm, taking into account the grid’s capacity and minimizing connection costs.

Many landforms included in the BDOT10k were deemed unsuitable for the location of a photovoltaic farm. The presented studies excluded, among other areas, surface water areas, including standing water, as unsuitable for the development of solar farms. In 2007 [77], the world’s first floating photovoltaic installation with a capacity of 20 [kW] was built. Since then, there has been a growing interest in the construction of this type of installations. This increased interest is mainly due to the limited availability of land on which traditional farms can be established and the higher efficiency of floating farms related to the cooling effect of water [78]. However, to assess the feasibility of installing a photovoltaic installation on a specific body of water, bathymetric and water quality studies should be performed, as well as the analyses of the geotechnical conditions of the banks and bottom of the reservoir [79].

Due to the lack of data on the width of transmission lines, the analyses did not exclude areas located beneath them. The literature reviewed indicates that these areas may be controversial. The construction of a PV farm may impede access to power lines, which is necessary for their maintenance or repair.

4.2. Sustainable Transition

The identification of areas with high suitability for the construction of photovoltaic farms, as derived from our own research, constitutes a significant contribution to the process of sustainable energy transformation. Locating investments in areas with adequate sunshine hours will optimize the use of available natural resources, which translates into increased efficiency of renewable energy generation. Designating such locations in regions with elevated levels of air pollution, such as Częstochowa Poviat, is particularly important, as the development of solar energy can contribute to reducing emissions of these pollutants, improving the environment, and improving the health of residents. Furthermore, the construction of new photovoltaic farms in Częstochowa Poviat will promote more sustainable spatial development of the country and thus reduce the risk of excessive concentration of energy infrastructure in selected regions.

The main barrier to the development of renewable energy sources in Poland is insufficient grid connection capacity [61]. Therefore, the highest priority of Polish energy policy should be the expansion and modernization of the national power grid. We therefore strongly recommend that future grid modernisation and expansion programmes are prioritised. Such investments would significantly enhance connection opportunities for individual cadastral parcels and accelerate renewable energy deployment across the region.

4.3. Feature Works

In future research, we will first extend the presented work with a sensitivity analysis to show how varying the weights affects the suitability outcome. This will allow for a better understanding of the robustness of the adopted criteria and the relative influence of each factor on the final site suitability map. Another practical next step would be to incorporate the locations of electrical substations and include proximity to these nodes as an additional criterion in the multicriteria analysis. The BDOT10k database shows that the Częstochowa Poviat (1519 km²) currently hosts 12 substations. Adding distance to substation as a variable would almost certainly reshape the existing land suitability map. The presented methodology can be further extended to include an analysis of the costs associated with purchasing plots of land for the construction of solar farms. This will provide a basis for initiating an investment feasibility analysis.

An interesting issue may be the expansion of research related to the shift in the country’s energy production profile to a more spread-out pattern throughout the day. This is related to the recently emerging problem of curtailment, or the temporary limitation of photovoltaic farm operation, particularly during peak generation around noon. This change may require, for example, investments in PV systems located in areas with East–West exposure [12].

Furthermore, extending the existing research to include agrovoltaics is a promising direction for future analyses [80]. Integrating electricity production with agricultural activities enables efficient land use while increasing the economic profitability of the investment. Incorporating criteria related to agricultural crops, such as light requirements, water access, and growing seasons, allows for a more comprehensive assessment of land suitability for PV installations. Furthermore, implementing the agrovoltaic concept can contribute to increased social acceptance of PV investments and support sustainable development strategies for rural regions, combining the goals of renewable energy production and food security.

5. Conclusions

The conducted research provides a comprehensive methodological framework for identifying and evaluating land suitability for photovoltaic (PV) farm development using GIS and fuzzy logic-based multicriteria analysis. The integration of climatic, topographic, land cover, and cadastral data results in a transparent and replicable decision-support model for spatial planning in the renewable energy sector. Notably, the study delivers ready-to-use geospatial outputs in the form of individual parcels and parcel complexes. This can directly support local authorities, investors, and spatial planners in evidence-based decision-making regarding the siting of new renewable energy installations of this type.

The study identified the most favorable locations for PV farms in Częstochowa Poviat—eight individual plots and 32 complexes consisting of at least two plots. The most favorable areas are located in the eastern part of the study area, in the municipalities of Koniecpol, Dąbrowa Zielona, Lelów, and Przyrów. Suitable land plots can also be found in the northern municipalities of Kruszyna, Kłomnice, and Rędziny, and the western municipalities of Konopiska and Poczesna. In the remaining municipalities of Mykanów, Mstów, Olsztyn, Janów, Kamienica Polska, Starcza, and Blachownia, no suitable plots for PV farm construction were identified. All plots and complexes deemed suitable for this type of investment meet a number of requirements (minor exceptions described in Section 4) justified by the relevant literature and legal provisions:

• Located in areas with relatively high solar irradiation;
• The terrain slope is no greater than 4%;
• The dominant exposure is south, southeast, or southwest;
• No soil class higher than IV;
• The straight-line distance to roads is no greater than 841 m;
• The distance to medium-voltage power lines is no greater than 312 m;
• The distance to residential buildings is no less than 60 m;
• The area of the plot/complex is no less than 1.5 ha;
• The shape factor of the plot/complex is within the range <0.82, 1.042>;
• The shortest side of the plot/complex is no less than 50 m.

Furthermore, the complexes were differentiated by the number of plots comprising the complex to allow for the selection of areas with the least formalities. A smaller number of plots typically means fewer owners, and therefore fewer formalities that must be completed during the purchase/lease process.

According to the reviewed literature, 40 different locations were identified in Częstochowa Poviat, each suitable for a PV farm with an estimated capacity of 1 MW. Of course, the next necessary step is to verify the technical capabilities of the nearby power grid, as the authors suggested in the Section 4. Nevertheless, the results of the study represent a milestone towards a sustainable energy transformation in a region of Poland requiring such changes. It is recommended that the results of this study be used to support the sustainable energy transition in regions of Poland that are in particular need of decarbonization and renewable energy development. The generated spatial data should be incorporated by local governments into local spatial development plans (MPZP) and renewable energy strategies to guide evidence based decision-making. Furthermore, the proposed methodological approach can inform regional and national energy policies and should be considered as a transferable model for sustainable land use planning in other regions, thereby supporting the broader transition toward renewable energy.