Most of the studies that have analysed the responses of citizens to PLEs have been in the fields of psychology and engineering [79; 80]. The experts who carried out these studies used questionnaires, tests, and/or rating scales, which did not take into account the parameters of luminous design that address the needs of local inhabitants. In these studies, the lighting evaluation attributes were determined in advance by researchers/experts, and the participants were mostly non-experts in lighting. This procedure has a significant limitation: the mental schemes of the non-experts/users are not considered in the questionnaires. [PERSON] and [PERSON] [81] found that participants employed terms mainly related to brightness and clarity to describe the lighting in rooms, and concepts such as pleasantness and colourfulness were mentioned only rarely. The concepts defined by lighting experts may not be fully understood by non-experts, such as local residents, in the case of PLE. Participants' uncertainty about the meaning of concepts may cause incorrect results or an increase in the variance of responses [82]. Furthermore, non-experts may never have the opportunity to rate certain attributes that they value highly because experts filter the rating information. In addressing this issue, KANei Engineering (KE), also known as Affective Engineering [83], a technique capable of translating users' feelings into concrete design elements, can be very effective. Its main advantage over other preference analysis techniques (QFD, Conjoint Analysis, etc.) is that KE provides a framework for operating with users' symbolic attributes and perceptions communicated in their own words. The KE methodology develops questionnaires based on concepts understood by users, not those understood solely by experts. In order to define these concepts, KE uses the Semantic Differential (SD), which was elaborated by [PERSON] et al. [84]. This technique involves selecting attributes or adjectives and establishing relationships between those attributes and design parameters. SD analyses the correlation matrices of scores assigned to different terms across a given set of products. By demonstrating that only a small number of dimensions or factors are enough to distinguish between the meanings of the whole set of concepts, these dimensions form a semantic basis for describing any product. This semantic basis is called a semantic space, and each concept is represented as a semantic axis. SD is a reliable and valid quantitative research method used to assess the affective connotations associated with concepts [85]. In KE research, SD is widely used to evaluate product perception [86; 87; 88; 89; 90]. KE operates on the premise that people's evaluations are influenced by stimuli, which consist of both objective and subjective parameters, as well as the conceptual schema of particular user groups (semantic space). This need has been evidenced in many areas of product design, where it has been shown that concepts can be perceived differently by different groups. These perceptions vary depending on the way of life, cultural background, and personal needs of the participants. This conceptual scheme features a collection of independent concepts (semantic axis or affective impressions) used by the users in describing their impressions of a product. KE has many applications, especially in product development [89; 91; 92; 93], including in the assessment of lighting equipment and uses [94; 95; 96]. In the field of environmental evaluation [97], it has been used to examine acoustic and sound perception [98], thermal [99], and classroom environments [100]. Nonetheless, to the best of the authors' knowledge, while KE has been applied in many contexts, it has not been applied to PLE to measure residents' responses. In this sense, it should be noted that [PERSON] and [PERSON] [51] analysed observers' emotional responses to urban lighting and made subsequent design recommendations.