1 Introduction
The escalating severity of climate change has prompted the global consensus on reducing greenhouse gas emissions, making the reduction of fossil fuel usage and the shift toward clean energy an inevitable trend in global energy development
[1]. As an efficient and feasible approach to solar energy utilization
[2], photovoltaic (PV) systems have been witnessed a rapid development in recent years under policy guidance worldwide
[3]. According to data from the European Commission, global installed PV capacity reached 1,608 GWp from 2010 to 2023, with China contributing approximately 671 GWp (42% of the global total)
[4]. With the growth of rural economies and the improvement of living standards, rural areas are becoming the primary source of future electricity additions and major regions for electricity demand in China. Surveys indicate that the total installable capacity of rooftop PV in rural China is 197 million kW, demonstrating vast development potential
[5]. Meanwhile, various "PV plus" production models in rural areas, such as agro-PV, fishery-PV, pastoral-PV, and forestry-PV ones, have significantly enhanced the comprehensive utilization of rural resources
[6][7].
Compared with urban areas, rural regions typically possess natural ecological environments that are more subject to the potential impacts of PV facilities. Although PV energy offers significant environmental advantages over traditional fossil fuels, large-scale PV facilities may lead to issues such as extensive land occupation, changes of rural landscapes, and local ecological impacts, which have gradually attracted widespread societal concerns
[8]. Countries and regions such as the Netherlands
[8] and Canada
[9] have promoted the construction of large-scale renewable energy infrastructure, but the caused visual intrusion has triggered strong public opposition. Against this backdrop, scholars have proposed many methods to assess the visual impact of large-scale renewable energy infrastructure
[10]~[12], and evaluation approaches initially developed for wind power infrastructure have been adapted to studies on large-scale ground-mounted PV systems
[13]. Researchers have proposed quantitative indicators and calculation methods for visual impact based on visibility, color, fractality, and concurrency. For example, Chiabrando Roberto et al. used OAI
SPP, a visual impact assessment tool, to measure the visual impact of PV plants on landscapes
[14].
In early studies on PV facility visual assessment, most research was conducted under the assumption that their visual impacts were inherently negative and required mitigation, and that PV facilities must be concealed. There was little discussion on how to actively integrate PV facilities into the landscape. In 2016, Alessandra Scognamiglio et al. introduced the concept of "photovoltaic landscapes" through inclusive design methods
[2], integrating PV systems as part of landscape design to reduce their visual impacts. In 2021, Dirk Oudes et al. proposed the idea of "solar landscape" which a spatial arrangement combining solar power plants and landscapes
[15]; in 2022, they further established a conceptual framework of "solar energy landscape" covering sustainable technology, economy, environment, and social aspects
[16].
Since 2003, Chinese scholars have begun exploring the integration of PV systems with the landscape
[17]. Research primarily aimed at promoting PV facilities, with topics covering the integrated design of PV systems with landscape elements
[18], the application of digital PV technology in rural landscape facilities
[19], planning and development modes for PV towns
[20], and construction modes for PV eco-agricultural leisure parks
[21]. Currently, national policies have made clear requirements for PV system site selection, mandating avoidance of cultivated land, ecological protection red lines, historical and cultural conservation zones, and natural forests and water bodies
[22][23]. However, systematic research on the visual impact of PV facilities on rural landscapes remains scarce, and relevant regulatory details are incomplete, resulting in a lack of specific landscape coordination guidelines for practice. This poses challenges for rural areas to balance renewable energy development with landscape protection when promoting PV facilities. There is an urgent need for scientific assessment of the visual impact of PV facilities on rural landscapes so as to inform policy formulation.
The visual Q-method, as a research approach combining subjective perception and quantitative analysis, sees efficient and intuitive advantages in the field of landscape evaluation and planning. This method was first applied to landscape evaluation and planning by Ervin H. Zube et al. and has further been adopted into diverse research contexts
[24]. For example, Simon Swaffield and John R. Fairweather used the visual Q-method with landscape photographs to investigate tourists' attitudes toward the effects of land use changes in New Zealand
[25]; they also employed the visual Q-method to explore differences in visitor experiences with landscape photographs
[26], demonstrating the method's effectiveness in tourism experience research; Andra Ioana Milcu et al. used the visual Q-method to explore diverse landscape preferences and potential conflicts among residents in rural Romania
[27]. Regarding visual impacts of PV facilities, Simona Naspetti et al. collected landscape images of various PV plants in Italian urban and rural environments and used the visual Q-method to explore their impact on local landscapes and land use
[24]; Ming Lu et al. applied the Q-method to investigate the impacts of PV systems on urban landscapes across different land-use types
[28]. Based on the aforementioned studies, this study employs the visual Q-method to systematically assess the visual impact of PV facilities on rural landscapes in China, providing a scientific basis for the sustainable development of PV infrastructure in rural areas.
2 Methodology and Data Collection
2.1 Overview of the Q-Method
British psychologist William Stephenson first proposed "Q-Methodology" (Q-method hereafter) in the 1930s and elaborated on it in his 1952 article
Q-Methodology and the Projective Techniques[29]. The Q-method is a research approach combining quantitative and qualitative methodologies. Differing from traditional statistical methods that rely on large-size random samples, the Q-method emphasizes intensive testing on a small number of participants or deriving extensive feedback from participants
[30]. Since the 1980s, Q-method research has expanded from psychology to disciplines such as health sciences, nursing, policy science, public administration, and environmental economics
[30].
A Q-method study typically involves five steps
[31]. 1) Defining research topic; 2) selecting Q-sample: the Q-sample should comprehensively cover all possible perspectives on the research topic; 3) selecting P-sample (i.e., participants): the chosen participants should be able to provide insightful or critical views on the research topic, and their composition and quantity should be determined by specific needs; 4) Q-sorting: assisting participants in understanding research objectives and completing the sorting of the Q-sample; and 5) factor extraction and interpretation: inputting participants' scores into statistical software and interpreting the analysis results.
The Q-method serves as a bridge between human perception and the environment
[28]. It should be noted that Q-samples may include not only textual materials but also ensembles of expression forms such as paintings, artworks, images, music, and videos
[32]. While textual Q-samples remain the most common form in current research, other forms offer their advantages. For example, the one uses visual stimuli as Q-samples is termed "visual Q-method"
[33]: using images as Q-samples provides participants with more intuitive, authentic scenarios, enabling them to recall associated emotions, memories, and thoughts that may be difficult to articulate verbally, thereby enhancing the reliability of interviews.
2.2 Research Design
2.2.1 Defining Research Topic
Based on the research objectives, the research topic of this study is employing the visual Q-method to investigate the visual impact of PV facilities on rural landscapes.
2.2.2 Selecting Q-Sample
In this study, photographs reflecting PV facilities and the surrounding environments were selected as Q-samples. The selection of Q-samples was determined by two aspects: the rural landscape typology
[34] and the application forms of PV facilities
[24]. Rural landscapes can be classified into natural landscapes, settlement landscapes, and production landscapes
[34]. Natural landscapes encompass rivers, vegetation, mountains, and nature reserves within rural areas. Settlement landscapes refer to architectural spaces closely related to human habitation. Production landscapes refer to the landscapes integrating labor activities and outcomes, including agricultural, forestry, and pastoral landscapes
[34][35]. Application forms of PV facilities can be categorized as integrated installations and standalone installations
[24]. In rural areas, integrated PV installations are typically attached to or embedded within rural structures (e.g., rooftops, facades). Standalone PV installations often appear as separate PV panels or sculptural installations
[36]. Images of rural PV landscapes across China were collected and filtered by removing duplicates or unrepresentative examples
[24], resulting in 36 finalized Q-samples which covers six categories: integrated PV installations in natural landscapes, Images 1 ~ 6; standalone PV installations in natural landscapes, Images 7 ~ 12; integrated PV installations in settlement landscapes, Images 13 ~ 18; standalone PV installations in settlement landscapes, Images 19 ~ 24; integrated PV installations in production landscapes, Images 25 ~ 30; and standalone PV installations in production landscapes, Images 31 ~ 36.
2.2.3 Selecting P-Sample
To ensure diversity in views, two groups of participants were recruited: experts (with knowledge about PV facilities and rural landscapes) and non-experts. The expert group comprised landscape architects, architects, planners, and PV engineers, while the non-expert group included villagers, rural tourists, and other interested individuals who were interested in this study.
Q-method does not specify strict requirements for P-sample size. Generally, the number of participants is less than Q-samples
[37]. However, considering potential invalid responses, the initial P-sample pool was expanded in this study. Besides, given experts' influence on rural PV policies
[24], the expert group (24 participants) slightly outnumbered the non-expert group (17 participants). Data were collected through face-to-face and online interviews from October to November 2023. After removing invalid responses (e.g., errors, duplicates, missing data), 34 valid datasets were obtained: 21 from experts and 13 from non-experts.
2.2.4 Q-Sorting
A normal-distribution scoring grid (Fig.1) was provided for participants
[38]. Scores ranged from −5 (least preferred) to +5 (most preferred), with 0 indicating neutral. Participants assigned image numbers into the grid according to their aesthetic preferences. Researchers explained the research objectives for participants to minimize biases from photographic techniques or image presentation. Participants were first asked to categorize images into three groups—liked (15 images), disliked (15 images), and neutral (6 images)—and then ranked them from "least preferred" to "most preferred" across all the grid cells; finally, participants were asked to provide brief explanations for their most and least preferred images for subsequent factor analysis.
2.2.5 Factor Extraction and Interpretation
Firstly, the 34 sets of scoring results from were processed with PQMethod2.35, a statistical software, and parameters such as column range, depth of column, and sorts entered were set. Then, principal components factor analysis was used for data standardization, resulting in a table of factor eigenvalues. According to the Kaiser criterion, a total of 8 datasets with eigenvalues no less than 1 were selected
①[37]. Considering the principles of ensuring the structural clarity and variance explanation of factors and minimizing the number of factors
[37][39], the research finally retained 5 datasets, i.e., the 5 major factors that visually impact the landscape by PV facilities in rural areas (Tab.1). These 5 factors together explained 49% of the research variance, which met the requirements of this study. Finally, the varimax rotation of the factors was used to correspondingly calculate the factor loadings, which represent the correlation strengths between the samples and the factors; and the meanings of these 5 factors were interpreted as well.
① Eigenvalue (EV) refers to the statistical strength and explanatory power of a factor. Factors of EV values no less than 1.00 were extracted for analysis in this study.
The interpretation of the factors was mainly based on the semantic elaboration of each image, the image rankings, and the comments from the interview records, particularly through the semantic extraction of the reasons for the "least preferred" and "most preferred" choices. After qualitative analysis and researchers' interpretation, the meanings represented by each factor were summarized.
The factor loadings of P-sample on each factor must satisfy the formula:
where F represents the loading of the factor in the P-sample, and n represents the number of P-sample. When the absolute value of F was greater than or equal to 0.43, the P-sample had significance on this factor (Tab.1). Then participants with similar views were categorized to revealing the concerns of expert and non-expert groups to different factors (Tab.2): The expert group paid more attention to Factors 4 and 5, while the non-expert group was more interested in Factor 2. Both groups showed less interest in Factor 3, while Factor 1 received common attention from the both groups.
3 Results
Through analysis and interpretation of each factor, the researchers defined the five factors as boundary integration, morphological innovation, color richness and coordination, multifunction, and scale.
3.1 Factor 1: Boundary Integration
Nine experts and four non-experts showed significant correlations with this factor, making it the factor received the most attention of participants. It also has the strongest explanatory power, accounting for 15% of the variance explained. This factor reflects participants' emphasis on the harmonious coexistence of PV facilities with their surrounding environment, which can be enhanced through appropriate boundary design and adaptive arrangements. For example, Image 1 depicts a lakeside PV corridor in a village, where PV panels are arranged along the lakefront path, highlighting a dynamic aesthetic. Image 11 illustrates a rural fishery-PV project, where PV arrays are arranged in accordance with existing terrain variations, minimizing disruption to natural fabrics while optimizing land use efficiency. In contrast, PV facilities in Images 9, 12, and 33 exhibit rigid boundary design that lack harmonious integration with natural surroundings (Fig.2).
3.2 Factor 2: Morphological Innovation
This factor accounted for 13% of the variance explained, ranking the second strongest explanatory power factor among the five. High-scoring images (e.g., Images 5, 6, 29) reveal that participants favored PV facilities with soft curvilinear forms, which combine functionality with artistic beauty. Conventional PV arrays that are often of monotonous designs (e.g., Images 9, 33) received lower scores due to their failure to meet contemporary aesthetic expectations. These findings suggest that innovative morphological designs can enhance public acceptance, with advancements in PV materials being critical to such innovations. As the emergence of thinner, flexible, transparent, or even bendable PV materials, PV facilities can better harmonize with rural natural landscapes through careful design (Fig.3).
3.3 Factor 3: Color Richness and Coordination
Accounting for 9% of the variance explained, this factor reflects participants' preference for vibrant colors or environmentally harmonious color schemes of PV facilities. For instance, Image 25 (colorful PV facilities) and Image 6 (color-coordinated designs) received high ratings. Image 6 features a cadmium telluride PV glass walkway in a mountainous rural area, where the color varies dynamically when the viewing angle changes. Conversely, PV materials dominated by black or dark blue hues (e.g., Images 1, 7) were less favored by participants. Designers and engineers are advancing color innovations of PV materials, creating visually appealing and effectively practical installations. Beyond thin-film PV materials, blue, green, and red cover glasses are now produced by crystalline silicon technologies with spectrally-selective reflection films
[40]. Moreover, domestically-produced colored PV cells possess both efficient performance and attractive appearance
[41], and colored semi-transparent cells surpass colorless ones in efficiency threshold
[42] (Fig.4).
3.4 Factor 4: Multifunction
Accounting for 7% of the variance explained, this factor highlights participants' preference for multifunctional PV facilities. High-scoring examples (e.g., Images 3, 26, 31) integrate energy generation with land use efficiency, increasing the utilitarian value of space. In contrast, low-scoring facilities (e.g., Images 9, 33) often serve single purpose or neglect multifunctional potential. Future, PV facilities should emphasize comprehensive benefits (e.g., multifunctional PV plants
[16]), contributing not only to energy transition but also to tourism, agriculture, and cultural initiatives (Fig.5).
3.5 Factor 5: Scale
Accounting for 5% of the variance explained, this factor reflects participants' focus on PV facilities' scale in space or mass. As a low-density planar energy form, PV facilities require substantial land for ensuring energy output
[43][44]. For instance, Image 7 displays a large-scale PV installation on rural highlands, where panels are arranged along the mountainous terrain to maximize solar absorption, enhancing the power generation efficiency. Lower-scoring cases are often smaller installations (e.g., Images 16, 20) and serve simply local energy needs
[45]. Nevertheless, large-scale deployments may conflict with other land uses
[46], necessitating systematic design and optimization schemes (Fig.6).
3.6 Factor Correlation Analysis
Correlation analysis (Tab.3) revealed the strongest positive correlation (0.5321) between Factor 1 (boundary integration) and Factor 2 (morphological innovation), with a large-percentage overlap of high-scoring images. These two factors also exhibited the strongest variance contributions, underscoring their centrality to PV facilities' visual impact on rural landscapes. Factors 1 and 2 represent two pathways to mitigate the visual degradation of rural natural landscapes caused by traditional linear PV layouts
[10]. Factor 2 advocates reducing visual abruptness through innovative morphological design. As an alternative, Factor 1 emphasizes mitigation visual conflicts through boundary-blurring measures such as aligning PV layouts with natural contours and planting native vegetation to soften their edges
[47]. Factors 3 and 4 showed the strongest negative correlation (−0.1598), which can be considered slightly correlated though, indicating that the negative correlation trends between the factors were not obvious.
4 Discussion
Based on the findings, this study discusses participants' visual satisfaction levels across three rural landscape types and then proposes strategies for PV facility planning and design.
4.1 Visual Satisfaction Levels Across Rural Landscape Types
Participants' visual satisfaction levels were analyzed by comparing the top 6 (considered "satisfied") and bottom 6 (considered "dissatisfied") images of each rural landscape type. Natural landscape images were scored "satisfied" 19 times and "dissatisfied" 14 times; settlement landscape images garnered "satisfied" only twice and "dissatisfied" 13 times; and production landscape images rated "satisfied" 9 times and "dissatisfied" 3 times.
Natural landscape images elicited the strongest polarized responses, suggesting that PV facilities in natural landscapes heightened strong emotional reactions and a significant disparity among the public. Notably, 53% of higher-rating images featured natural landscapes, suggesting they received a higher visual acceptance. However, natural landscapes also dominated the least preferred images. Participants disliked large standalone PV facilities (e.g., Images 8, 9) in natural landscapes, which disrupt ecological continuity and degrade natural aesthetics. Thus, introducing PV facilities into natural landscapes requires meticulous caution.
In terms of settlement landscape images, the frequency of satisfied images across various factors was the lowest, accounting for only 5% of the total, which was significantly less than the frequency of the dissatisfied ones. This reveals that such PV facilities ranked the lowest satisfaction among participants. By comparing the dissatisfied samples (e.g., Images 22, 23, and 24) with the satisfied ones (e.g., Image 15), it was found that the public reported a higher acceptance of PV facilities in rural settlement landscapes with aesthetic quality in design. However, most of the current PV facilities in rural settlement landscapes focus solely on their power generation by simply laying PV panels on rooftops or the ground, neglecting their aesthetic value, which led to negative evaluations from the participants.
For the images of PV facilities in production landscapes, the frequency of satisfied images across various factors was lower, accounting for 25% of the total, while that of the dissatisfied images was the lowest. This suggests that participants had a moderate satisfaction level about such PV facilities. Overall, the frequencies of both the satisfied and the dissatisfied were the lowest, indicating that when PV facilities are integrated into rural production spaces, their visual impacts on surrounding landscapes would be relatively low.
4.2 Planning and Design Strategies for Rural PV Facilities in Rural Landscapes
The planning and design for PV facilities in rural areas should prioritize ecological harmony and landscape integration
[48][49], transforming PV systems into integral components of rural landscapes, beyond serving for energy generation demands. Integrating PV facilities into natural landscapes requires comprehensive assessments in ecology, aesthetics, and economy. Pre-planning steps—land use surveys, environmental assessments, and topographic mapping
[50]~[52]—ensure that PV facilities harmonize with existing landscapes, balancing energy needs with ecological and social benefits.
In rural natural landscapes, the morphological innovation of PV facilities can be achieved by extracting regional cultural symbols to create PV facility forms that are harmonious with the surrounding environment. For instance, in Image 5, PV panels are designed as "leaves" of the local banyan trees, which softens the industrial image of traditional PV facilities and improves their visual harmony with the surrounding natural vegetation. In terms of color richness and coordination, it can be achieved by analyzing the color composition of the local natural landscapes and extracting the main and auxiliary color hues. For example, Image 2 features a colorful rainbow corridor built in a sunflower field, achieving a harmonious landscape of PV facilities with the natural environment. The boundary integration of PV facilities can be realized through layout designs that coordinate with natural topography, vegetation, and cultural landscapes, blurring the boundaries of PV facilities into the surrounding environment. For instance, Image 8 displays PV panels arranged along the natural contours of the site, not only making full use of the terrain but also minimizing the impact of artificial intervention on the natural landscape, and furthermore, creating a unique landscape rhythm that enhances visual harmony.
In settlement landscapes, PV facilities should emphasize multifunctional designs that provide clean energy generation while meeting the needs of rural living, cultural expression, and ecological conservation. Image 14 exemplifies this strategy by integrating PV panels onto the roof of an information board, combining solar energy generation while keeping the board's original function, thereby merging energy supply with public services. Such multifunctional designs would transform PV facilities into a critical part of rural energy systems by embedding them into public life as landscape nodes for energy generation and cultural and ecological purposes.
In production landscapes, where PV facilities exert minimal visual impacts, priority should be given to the increase of scale efficiency. Large-scale deployments in ecologically low-sensitivity areas or underused lands to optimize spatial resource utilization efficiency. Image 34 illustrates a salt-PV project where PV arrays are installed above salt pans, preserving salt production while efficiently harnessing solar energy.
5 Conclusions
This study systematically investigated the visual impact of PV facilities on rural landscapes using the visual Q-method and identified five factors: boundary integration, morphological innovation, color richness and coordination, multifunction, and scale. Analysis of the two participant groups revealed that non-experts paid more attention on color richness and coordination, whereas experts focused more on boundary integration and multifunction; and both groups emphasized morphological innovation. Correlation analysis indicated that the five factors are interconnected, with the factors of morphological innovation and boundary integration exhibiting the strongest positive correlations and the widest consensus among participants. The study further discussed the visual satisfaction levels across different rural landscape types and proposed planning and design strategies for PV facilities in rural areas.
Finally, this study has certain limitations. First, in the Q-sorting step, despite the participants were instructed to minimize biases from photographic techniques and image presentation of the Q-samples, their choices were inevitably influenced to some extent. Second, online and offline data collection approaches were employed in this study and the guidance for online participants was insufficient, leading to a higher proportion of invalid online responses during data screening. Therefore, offline questionnaires are recommended for future studies to ensure data quality and consistency.
PDF
(2832KB)
