Smart cities as catalysts for intergenerational well-being, sustainability and urban safety: a case study from Ljubljana, Slovenia

SCs represent a paradigmatic shift in the development of urban areas, harnessing technology, AI-driven decision-making and cognitive digital twin systems to optimize resource management, infrastructure efficiency and resident well-being. By integrating principles of sustainability, public health considerations and human–system partnership models, SCs hold the potential to reduce environmental impact, promote healthy lifestyles and foster inclusive communities. Smart urban environments deploy advanced technologies, human-in-the-loop methodologies, shared autonomy between AI and human decision-makers and real-time data integration to create more efficient, liveable and environmentally responsive urban settings (Giffinger et al., 2007; Yin et al., 2015). The term human-in-the-loop refers to systems in which human judgment and decision-making remain integral to AI-driven processes, ensuring ethical oversight, adaptability and contextual relevance (Ristvej et al., 2020).

Recent conceptualizations of SCs emphasize their role not only as technological infrastructures but as dynamic systems shaped by governance models, social participation and inclusive urban design (Wang and Zhou, 2023). In this view, the “smartness” of a city is co-constructed through both top-down implementations (e.g. AI-enabled services, data-driven planning) and bottom-up user experiences that vary according to age, socio-economic status and digital literacy. This study builds on these frameworks by examining how SC features are perceived rather than only deployed, and how these perceptions reflect generational positioning within the urban system.

Recent research published in Smart and Sustainable Built Environment further underscores the need for integrated mobility governance as a foundation for resilient and health-oriented urban systems. Smart mobility policies are increasingly recognized as catalysts for sustainability and public safety, linking environmental quality, transportation equity and community well-being. In this context, Anthony Jnr (2025) developed a governance-based conceptual model positioning sustainable mobility as a key determinant of SC resilience and highlighting the importance of multi-level collaboration among policymakers, urban designers and residents.

While SC offer significant opportunities for advancing sustainability and public health, they also present critical challenges that must be addressed. Human–system partnerships should be designed to ensure that automation enhances rather than replaces human autonomy, thereby preventing urban environments from becoming overly mechanized and disconnected from human-centred values across all life stages. Existing research often frames individual well-being as the primary indicator of sustainable SC development. However, such a narrow focus risks overlooking intergenerational justice and long-term urban resilience. Building on established sustainability and well-being frameworks, this study adopts an intergenerational perspective to examine whether perceived smart urban environments support well-being equitably across life stages. In this paper, smart urban space is treated as a socio-technical setting in which technological affordances interact with residents’ needs and vulnerabilities rather than as a deterministic or uniformly transformative driver of well-being.

It is important to acknowledge that the experience of smart urban environments is shaped not only by age but also by intersecting dimensions of social identity, particularly gender. Research indicates that women and men often perceive and utilize urban spaces differently, with gender-differentiated patterns of mobility, safety concerns and access to digital services (Pain, 2000). Women, especially older women, report higher levels of fear of crime and are more likely to modify their behaviour in public spaces accordingly (Valentine, 1989). Similarly, gender disparities in digital literacy and technology adoption can influence how individuals engage with SC infrastructures (Hilbert, 2011). In recognition of these established differences, gender was also included as an additional analytical factor in this study to assess whether men and women differ in their perceptions of health, safety and environmental quality within self-identified smart urban environments. While the observed gender effects were modest, their inclusion enhanced the explanatory scope of the analysis and underscored the importance of intersectional perspectives in understanding urban experience.

The core premise of this research is that we can no longer design environments that prioritize short-term efficiency and individual well-being at the expense of long-term sustainability. Such approaches ultimately deplete resources and undermine environmental resilience. Accordingly, cities increasingly need adaptive, AI-supported approaches that are transparent, inclusive and responsive to residents’ lived experiences across age groups. We live in an era in which algorithmic bias, data surveillance and opaque governance structures increasingly contribute to public anxiety (Grum and Kobal Grum, 2023a). Nevertheless, emerging studies indicate that hope, human-centred AI and transparent automated systems can strengthen psychological resilience, foster trust and support long-term well-being (Scheier and Carver, 1992).

Well-being and perceived health are closely linked to the quality and safety of the urban environment (Grum Kobal and Grum, 2023a), which can foster a sense of joy in everyday life, namely, hope and optimism, which are key components of sustainable and inclusive SC development. This intergenerational concept is the central focus of our study. We investigate how intergenerational perspectives shape urban well-being, digital adaptation and city-wide resilience supported by AI, as assessed through self-reported health and the perceived quality of the living environment. This includes factors such as natural lighting, air quality, noise exposure, access to green spaces and sense of safety.

While generational differences in well-being, environmental sensitivity and perceived safety are well-documented in the literature (Boyce, 2014; Steptoe et al., 2015), our study aims to examine whether such differences acquire distinct expressions within self-identified SC contexts. The conceptual focus is not to attribute causality directly to smart technologies, but to explore how specific features of smart urban design, such as adaptive lighting, air quality monitoring and participatory safety planning, may shape perceived quality of life differently across generations. Prior studies suggest that SC technologies are not uniformly perceived or utilized, with older adults benefiting more from health and safety enhancements (Chen et al., 2024), while younger individuals often prioritize mobility and digital services (Kirimtat et al., 2020). Thus, this study operates within a descriptive and interpretive framework, aiming to illuminate how life-course-specific needs intersect with the infrastructural and technological affordances of SCs. Rather than claiming deterministic effects, we consider the SC context as a moderating environment in which age-related preferences and vulnerabilities are mediated by technological and spatial configurations.

This study is informed by a conceptual framework that integrates subjective well-being theory (Diener et al., 1999, 2018) with emerging models of SC inclusiveness (Neirotti et al., 2014). We posit that residents’ perceptions of health, environmental satisfaction and safety are shaped by both infrastructural factors (e.g. air quality, noise, lighting) and socio-psychological dimensions (e.g. digital literacy, age-related trust in technology). Generational positioning may mediate access to smart services and interpretive schemas of what constitutes a “smart” or “healthy” urban experience. The framework thus considers age as a proxy for differences in technological engagement, risk perception and environmental concern, which together influence residents’ self-reported well-being in SC contexts.

This study builds upon the empirical groundwork laid by Grum and Kobal Grum (2023a, b), whose research explored post-pandemic shifts in residential satisfaction, real-estate perceptions and urban spatial adaptations. While their work focused primarily on older adults’ experiences during COVID-19, the current paper extends this foundation by incorporating a broader demographic lens – explicitly addressing how digital equity and subjective well-being are mediated by generational differences within smart urban contexts. Recent systematic research underscores the need for equity-focused frameworks in SC planning. For example, a review of health-focused SC interventions found that most projects prioritized spatial or socioeconomic disparities, while gender, occupation, education and ethnicity were rarely included in evaluation models (Buttazzoni et al., 2020). This suggests a persistent neglect of intersectional factors in urban digital design.

Furthermore, the subjective experience of SCs is often missing in urban metrics. Using participatory “data walking” methods, Butot et al. (2023) reveal how urban residents interpret surveillance infrastructure differently depending on visibility, safety and agency, highlighting the importance of incorporating lived experiences into SC assessments. Design research by Blacutt and Roche (2020) similarly shows that inclusive technologies – such as those designed for deaf users – can enhance spatial capabilities and urban mobility, but only when disability and embodied experience are treated as core design variables, not afterthoughts (Blacutt and Roche, 2020).

In emerging economies, digital governance often prioritizes technological innovation and economic growth over social inclusion, resulting in policy frameworks that insufficiently address equity and participation. As Choi and Kenney (2024) demonstrate in their evaluation of Thailand’s SC policies, inclusive and participatory principles are rarely embedded at the national level and tend to emerge only through localized, bottom-up initiatives. Within this context, the concept of subjective well-being – originally grounded in foundational psychological models (Diener et al., 1999) – has gained renewed relevance as scholars seek to integrate human-centred indicators into urban digital infrastructures. Extending this perspective, Narain (2024) calls for a holistic approach to SC planning that unites environmental sustainability, quality of life and social inclusivity through digital infrastructure and data-driven technologies. Yet, as Narain cautions, most contemporary SC models remain technologically centred, focusing on measurable “hard” indicators such as Internet-of-Things connectivity and energy efficiency, while neglecting the participatory, behavioural and social dimensions that fundamentally shape urban well-being.

This paper offers an empirical, context-specific contribution to contemporary debates on inclusive SC by integrating generational and gender perspectives in a single survey-based analysis of self-identified SC residents in Ljubljana. It (1) maps how perceived health, environmental satisfaction and safety vary across life stages and gender; (2) shows that perceived well-being benefits are uneven across groups; and (3) translates these patterns into implications for age- and gender-responsive, human-centred smart urban planning. Importantly, the contribution is best understood as providing nuanced, context-sensitive evidence rather than proposing a fundamentally new conceptual framework.

SC research continues to debate whether “smartness” should be assessed primarily through objective technological capacity or through residents’ lived experience, especially when governance opacity, surveillance concerns and digital exclusion can weaken trust and perceived well-being even in technologically advanced contexts. Building on this debate, we treat Ljubljana’s self-identified SC residents as informants of perceived smart-urban experience, and we operationalize our framework via two SC components that are most directly tied to sustainability and health: (1) “Environment and Sustainability”, captured through satisfaction with air quality, natural lighting, noise levels and access to green spaces; and (2) “Living/Safety and Health,” captured through self-rated health and perceived safety. This mapping allows us to test whether well-being–relevant perceptions are distributed evenly across life stages and gender, without implying that smart technologies themselves are the direct causal driver of these outcomes.

SC research increasingly foregrounds resident well-being alongside technological efficiency (Kirimtat et al., 2020). To manage the complexity of holistic SC governance, scholars often disaggregate the concept into dimensions and components (Albino et al., 2015). Building on this approach, Trincă (2023) synthesizes key SC dimensions (Figure 1), which we use as a reference point for selecting the components most relevant to sustainability and health.

Within the framework of the diagram proposed by Trincă (2023), our study focuses on two key components of SCs: “Living/Life/Safety and Health” and “Environment and Sustainability”. The first component encompasses various aspects of perceived individual quality of life, which can be enhanced through the redesign of living spaces and spatial planning strategies aimed at creating more sustainable and liveable urban environments (Stegerean et al., 2022). Smart living fosters public awareness of how social dynamics and technological progress interact to improve everyday life. As such, smart living integrates elements that contribute to a fulfilling and enjoyable lifestyle (Anthony Jnr, 2021). In the development of SCs, it is essential to consider not only technological advancement but also broader social and environmental factors that affect residents' well-being (Shao and Min, 2025). Through thoughtful planning and the ethical implementation of technology, SCs can serve as a driving force for sustainable and healthy urban environments that meet the needs of all generations.

The Environment and Sustainability component plays a central role in the design of SCs, as it supports cities' long-term resilience to climate change, emission reduction and more efficient resource use (Grum and Kobal Grum, 2023a). The expansion of green areas, rooftop gardens and urban forests contributes to mitigating the urban heat island effect and improving air quality (McCormick et al., 2013). SCs aim to holistically enhance the urban environment, with critical factors that directly impact health, including air quality, noise levels, access to green spaces and natural lighting. These elements significantly affect both public health and well-being, as well as the sustainable development of urban infrastructure (Kristl et al., 2025). Advanced technologies enable the precise monitoring and dynamic adjustment of these parameters, thereby improving the quality of life in urban settings (Ahvenniemi et al., 2017). Poor air quality remains one of the greatest challenges of contemporary urbanization, as it is strongly associated with increased incidence of respiratory and cardiovascular diseases (WHO, 2021). Smart urban lighting management plays a vital role in energy efficiency, public safety and overall liveability. Excessive artificial lighting contributes to light pollution, negatively affecting ecosystems and disrupting residents' biological rhythms (Falchi et al., 2016). Urban factors directly linked to health, such as natural light in housing, air quality and noise levels, serve as key indicators of smart living (Winkowska et al., 2019).

The need for a healthy and comfortable living environment is fundamental to both mental and physical well-being (Meško et al., 2012). A sense of safety, particularly as it relates to fear of crime, and the underlying causes of that fear are often more widespread than crime itself (Miceli et al., 2004). Austin et al. (2002) explored the relationship between neighbourhood conditions and residents' perceptions of safety. Their findings indicate that both the physical quality of housing and the broader physical condition of the surrounding environment significantly influence expressed satisfaction with perceived safety.

In this study, age was used as a key variable, with participants grouped into three generational cohorts. These were analysed in relation to their self-reported health status and their perception of a healthy living environment. This perception encompassed the key elements drawn from the two observed SC components: “Living/Life/Safety and Health” and “Environment and Sustainability”. The study included only those participants who self-identified their living environment as an SC. The central focus was to understand how generational differences manifest in self-assessed health, perception of environmental quality (including natural lighting, air quality, noise levels and access to green spaces) and sense of safety. Within this framework, an SC is understood not merely as a technological system, but as a complex socio-environmental ecosystem that must equitably integrate the diverse needs, values and vulnerabilities of all age groups. Only by doing so can SCs truly become catalysts for long-term well-being, urban safety and sustainable resilience.

Recent studies published in Smart and Sustainable Built Environment have highlighted the growing relevance of environmentally responsible technologies within the SC framework. Ullah et al. (2024) identified “Green Internet of Things” (G-IoT) applications as essential for improving energy efficiency, pollution control and environmental health, underscoring that technological advancement must be accompanied by sustainability-oriented design strategies. Furthermore, Alkhalifa (2024) demonstrated that residents’ readiness and perception significantly influence the successful transformation towards smart and sustainable city models, stressing the need to integrate social and behavioural dimensions into technological planning. The aim of this research is to examine whether statistically significant differences exist between age groups in terms of perceived well-being, self-reported health, satisfaction with environmental factors and sense of safety within self-identified SC. Understanding these generational differences is essential for policymakers and urban planners, as it enables the development of more inclusive, equitable and generation-responsive strategies for the future of sustainable urban development.

Before testing our hypotheses, it is essential to operationally define the core constructs that underpin this study and to clarify how they translate the above debates into empirically testable expectations. In this study, an SC is operationally defined as an urban environment that integrates advanced information and communication technologies (ICT), data-driven infrastructure and sustainability-oriented planning to enhance quality of life, resource efficiency and environmental responsiveness (Albino et al., 2015; Giffinger et al., 2007). Rather than relying solely on objective technological indicators, we adopt a perception-based approach: participants self-identify their living environment as an SC based on their subjective understanding of smartness, which may include exposure to digital services, environmental monitoring systems, intelligent mobility solutions and participatory governance platforms.

Well-being is conceptualized according to subjective well-being theory (Diener et al., 1999) as individuals' cognitive and affective evaluations of their lives. In this study, it encompasses self-rated health, satisfaction with living conditions, perceived environmental quality and sense of safety. Well-being is measured through self-report instruments that capture both hedonic dimensions (e.g. life satisfaction, positive affect) and eudaimonic dimensions (e.g. sense of meaning, environmental mastery). This approach acknowledges that well-being is context-dependent and shaped by both individual characteristics (e.g. age, health status) and environmental factors (e.g. air quality, access to green spaces).

Sustainability in the context of SCs refers to urban development that balances environmental protection, social equity and economic viability across generations (McCormick et al., 2013). Operationally, this study focuses on environmental sustainability indicators that directly affect residents' quality of life: air quality, natural lighting, noise levels and access to green spaces. These variables reflect SC-specific interventions such as pollution monitoring systems, energy-efficient building design, smart traffic management and urban greening strategies. Sustainability is thus understood not as an abstract ideal, but as a set of measurable environmental conditions that contribute to long-term urban resilience and health.

Age is employed in this study as a heuristic for understanding life-course-specific needs, technological engagement patterns and environmental sensitivities within smart urban contexts. The rationale for prioritizing age stems from three considerations: (1) life-stage specificity – different age groups face distinct developmental tasks, health vulnerabilities and social roles that shape their interaction with urban environments (Baltes, 1987); (2) technological adaptation – age is strongly associated with digital literacy, technology acceptance and usage patterns of smart services (Czaja et al., 2006; Venkatesh et al., 2012); and (3) environmental vulnerability – older adults exhibit heightened sensitivity to environmental stressors such as air pollution, noise and inadequate lighting due to age-related physiological changes (Boyce, 2014; Kelly and Fussell, 2015).

Guided by the conceptual framework outlined above, this study seeks to examine whether age groups differ in their perceptions of selected dimensions of smart urban living. Specifically, we test the following hypotheses:

These hypotheses are tested using one-way ANOVA and Tukey's Honestly Significant Difference (HSD) post-hoc comparisons across three age groups.

This study employed a quantitative, survey-based design to examine intergenerational differences in perceived well-being, environmental satisfaction and sense of safety in self-identified SCs. The data were part of a wider interdisciplinary study on smart urban environments and generational well-being (Grum and Kobal Grum, 2023a, b) and were analysed using descriptive statistics, reliability testing and one-way ANOVA with post-hoc comparisons.

A total of 473 Slovenian adults participated in the survey, most of whom resided in Ljubljana or its suburban surroundings. Participants were recruited using a non-probability snowball strategy (online invitations plus targeted in-person distribution). Because recruitment relied partly on social and university networks, the sample may over-represent more educated and/or more digitally engaged residents, which can bias estimates of perceived “smartness” and well-being. Accordingly, the results should be interpreted as comparative group patterns within this sample rather than as population prevalence for Ljubljana. Participants were recruited through a combination of online invitations (distributed via university mailing lists, community organizations and social media) and in-person dissemination in selected public settings (libraries, community centres and residential areas).

Ljubljana, the capital and largest city of Slovenia, has approximately 295,000 inhabitants (Statistical Office of the Republic of Slovenia, 2023), with a relatively balanced age distribution and high levels of urbanization. The city has actively promoted SC initiatives, particularly in mobility, energy management and digital public services, earning recognition through its “Ljubljana SC” strategy. Our final sample was selected to ensure age group variability rather than full demographic representativeness. While the sample was not proportionally stratified according to the city’s population profile, the use of snowball sampling across different districts helped capture a range of urban experiences within Ljubljana’s SC landscape. Although the sample represents approximately 0.16% of Ljubljana’s adult population, the size (N = 473) is adequate for detecting group differences. However, statistical power does not compensate for non-random sampling: inference is limited to associations within the recruited sample, and external validity depends on how closely the sample matches the wider population. We therefore avoid generalizing effect sizes to Ljubljana as a whole and treat findings as context-specific evidence that motivates replication with probabilistic or stratified samples.

Participants who rated the SC identification item below 4 (scores 1–3) were excluded to focus on perceived SC contexts. This strengthens conceptual alignment with our research question, but it also introduces a selection mechanism: residents living in objectively “smart” areas who do not recognize or accept the SC label are omitted. As a result, our findings speak to experiences among self-identified SC residents and may understate scepticism, distrust or exclusion that could be more visible in non-identifying groups.

Demographic variables such as gender, education and income were partially collected but not systematically analysed due to missingness and uneven distribution across age groups. This limits our ability to rule out confounding (e.g. socioeconomic status shaping both neighbourhood conditions and perceived well-being). We therefore interpret age- and gender-patterns as indicative rather than definitive, and we recommend future work incorporate fuller socio-demographic controls and intersectional designs. To minimize the variability in interpretation of the term “SC”, the survey introduced the concept with a short explanatory note prior to the key screening item. Participants were informed that SCs typically integrate advanced technologies, digital infrastructure and sustainability-oriented urban planning to enhance the quality of life. This clarification was intended to align participants’ understanding with internationally accepted definitions (e.g. Albino et al., 2015; Giffinger et al., 2007). Nevertheless, as perceptions of smartness can vary with age, experience and accessibility, we acknowledge that subjective understanding may still have influenced responses, a limitation discussed below.

Participants who rated the SC identification item below 4 (i.e. scores 1–3) were excluded from the final analysis to focus the study on those who perceived themselves as living in a smart urban environment. This decision was methodologically aligned with our aim to analyse differences within perceived smart contexts rather than to compare them with non-smart settings. However, we acknowledge that this exclusion removed potentially valuable insights from individuals who may be situated within objectively smart environments but do not perceive them as such. The survey did not include an open-ended justification for this item, which limits our ability to interpret the subjective basis for their disagreement. We recommend that future research include qualitative follow-up to better understand the conditions that shape perceived (non-)smartness across different populations.

Demographic variables such as gender, education and income were partially collected but not systematically analysed due to incomplete responses and uneven distribution across age groups. Ethnic background was not assessed, given the relative demographic homogeneity of the Slovenian population, particularly in Ljubljana. While the primary focus was age, we acknowledge that factors such as gender and socioeconomic status significantly influence urban experience, especially in relation to safety and access to smart infrastructures. The exclusion of these variables from the main analysis is a limitation that may reduce the nuance and generalizability of the findings.

Data were collected in two phases through a self-administered questionnaire available in both digital and paper formats. The survey was distributed via online channels and in selected public or institutional settings, allowing for broad accessibility and reducing potential barriers related to digital literacy. Participants were encouraged to share the survey with others across different age groups to promote generational representation. The questionnaire included sections on housing conditions, satisfaction with the local environment, neighbourhood infrastructure and overall urban quality of life. Participation was anonymous and voluntary. Prior to completing the questionnaire, participants were informed of the study’s aims and data handling procedures. Ethical approval was reviewed by the Ethics Committee of European Faculty of Law, New University, which confirmed that formal institutional approval was not required as the study involved only anonymous, non-invasive questionnaires. All procedures complied with institutional ethical guidelines and the Declaration of Helsinki.

The instrument was based on established survey methodology (Walonic, 2007) and adapted to reflect key dimensions relevant to SC. It consisted of demographic questions and thematic items measuring subjective well-being, environmental quality and urban safety. The primary independent variable was age, categorized into three generational cohorts: young adults (18–35), middle-aged adults (36–60) and older adults (61+). Dependent variables were measured on Likert-type scales, mostly ranging from 1 (very dissatisfied) to 5 (very satisfied). Self-reported well-being and health were assessed with a general item reflecting overall physical and mental status. Environmental quality was operationalized through participants’ satisfaction with natural lighting, air quality, noise levels and access to green spaces.

These indicators were selected based on their established role in SC frameworks (Ahvenniemi et al., 2017; Albino et al., 2015), where environmental sensors and data-driven infrastructure enable precise management of air quality, urban noise, lighting and green areas. For example, satisfaction with air quality reflects the effectiveness of pollution monitoring and mitigation systems typical of SC; natural lighting corresponds to energy-efficient and health-conscious building design; and access to green spaces is directly tied to urban planning strategies that prioritize environmental sustainability and mental well-being. Noise levels are similarly moderated through smart traffic regulation and urban zoning. These variables, therefore, operationalize the component “Environment and Sustainability” as measurable resident-centred experiences. The component “Living/Safety/Health” is captured through perceived safety and satisfaction with housing, which reflect smart surveillance infrastructure, inclusive public space design and health-supportive urban amenities.

To increase transparency and methodological clarity, Table 1 provides an overview of the key variables, their operational indicators and corresponding survey items used in the questionnaire.

The internal consistency of this scale was confirmed through Guttman’s λ2 coefficient, which yielded values of 0.891 (weighted sum) and 0.876 (unweighted sum) (Sočan and Kobal Grum, 2023), indicating high reliability.

Although the primary analysis focused on ANOVA and post-hoc comparisons to examine group-level differences by age, we also explored bivariate relationships among the main dependent variables using Pearson correlation coefficients. These additional analyses provided insight into the co-occurrence of satisfaction with various environmental factors (e.g. air quality and natural lighting) and their joint contribution to perceived well-being. Moreover, exploratory linear regression models were tested to assess the predictive power of environmental satisfaction and perceived safety on self-rated health, revealing moderate but significant associations (results available upon request). While demographic variables such as education and income were not included in the final model due to missing values and non-systematic collection, we acknowledge their potential role as covariates and discuss them in the limitations.

To strengthen methodological robustness and address inclusivity concerns, additional analyses of variance (ANOVA) were conducted to examine potential gender-based differences and interactions between gender and age. The inclusion of gender as a fixed factor allowed for the assessment of whether men and women differ in self-rated health, perceived environmental quality and safety within self-identified smart urban environments. This two-way ANOVA framework (Age × Gender) provided a more comprehensive picture of the social dimensions shaping residents’ perceptions of SCs, in line with intersectional approaches to urban well-being.

All analyses were conducted using IBM SPSS Statistics. Descriptive statistics were computed to summarize the sample’s demographic profile and responses to key measures. To verify scale robustness, Guttman’s λ2 coefficient was used to assess the internal consistency of the composite measure of satisfaction with residential property. To test for intergenerational differences, a one-way ANOVA was performed with age group as the independent variable. Dependent variables included self-rated well-being and health, satisfaction with environmental parameters (natural lighting, air quality, noise levels and access to green spaces) and perceived safety. For variables showing statistically significant differences (p < 0.05), Tukey’s HSD test was applied to identify specific group differences.

To align reader expectations with the manuscript’s positioning, this section interprets the findings as context-specific empirical evidence from Ljubljana rather than as a basis for broad theoretical innovation or universal claims about SC. The analysis identifies group differences in well-being–relevant perceptions within a self-identified SC sample and translates them into cautious implications for inclusive, human-centred planning.

In interpreting the results, we consider the measured variables not as general indicators of urban satisfaction, but as proxies for core SC components. For instance, intergenerational differences in satisfaction with air quality and natural light may indirectly reflect differential experiences with smart environmental monitoring or design, while perceptions of safety can be shaped, in part, by SC-specific interventions such as smart lighting, CCTV networks or data-informed policing as they are perceived and experienced by residents. By anchoring our interpretation in the conceptual framework of SC, we aim to differentiate between general environmental satisfaction and smart-technology-modulated urban experience. This interpretive move responds to an ongoing tension in the SC literature between “technology-first” framings and socio-technical accounts that emphasize lived experience, equity and the uneven distribution of benefits (Buttazzoni et al., 2020). Rather than assuming smartness as an intrinsic public good, we treat it as a contingent set of infrastructures and governance arrangements whose value depends on how different groups experience them.

Table 1 presents the demographic structure of participants according to age groups, while Table 2 illustrates statistically significant differences between age and key SC components.

Statistically significant differences according to the age and observed parameters are shown in Table 3.

The findings reveal statistically significant differences between age groups in self-rated health, satisfaction with environmental factors and perceived safety within self-identified smart urban environments. These distinctions suggest that SCs are not neutral spaces, but rather complex socio-technical environments in which user experiences vary considerably by age. This nuance is important because a persistent debate in the field concerns whether SC interventions systematically enhance urban well-being or whether they risk reproducing (or even intensifying) existing inequalities through differential access, differential literacy and differential exposure to surveillance and control (Butot et al., 2023; Buttazzoni et al., 2020; Choi and Kenney, 2024; Czaja et al., 2006; Hilbert, 2011; Venkatesh et al., 2012). Our results align more strongly with the latter cautionary position: perceived benefits are uneven across life stages, and “smartness” is experienced differently across groups (Buttazzoni et al., 2020; Neirotti et al., 2014; Wang and Zhou, 2023). In socio-technical terms, the same infrastructures and services are filtered through life-stage routines, resources and digital confidence, producing uneven perceived well-being even within a single urban context.

Overall, the findings supported all three hypotheses within this survey-based, Ljubljana-specific sample of self-identified SC-residents. Specifically, H1 was supported, as statistically significant age-based differences were observed in residents’ satisfaction with health-related living conditions, particularly regarding natural lighting and noise levels (p < 0.05). The testing of H2 examined whether environmental and sustainability-related features of the SC environment – specifically air quality, natural lighting, noise levels and access to green spaces – were associated with residents’ subjective well-being and satisfaction. The analysis confirmed statistically significant differences across age groups for all four environmental indicators (all p < 0.05), with older adults reporting the highest levels of satisfaction with green access and natural lighting and the lowest sensitivity to noise disturbance. These findings support H2, demonstrating that environmental quality remains a salient determinant of subjective well-being in SC contexts. Importantly, the results suggest that perceived environmental comfort and access to natural elements may be interpreted as potential pathways through which residents relate sustainability-relevant features to perceived well-being, without implying causal mediation. This finding speaks to a second tension in the literature: whether smart safety infrastructures (e.g. sensor networks, CCTV, predictive policing) are experienced as protection or as surveillance. While our data do not directly measure trust in governance or privacy concerns, the age-patterned differences in perceived safety are consistent with the broader argument that security technologies can generate divergent subjective experiences depending on perceived vulnerability, familiarity with digital systems and expectations about institutional legitimacy.

Here, our findings refine a common assumption in sustainability-oriented SC narratives: that environmental monitoring and “smart” optimization translate straightforwardly into improved lived experience. Instead, the results suggest that environmental “quality” is not only a technical output but also a perceptual and socio-spatial experience that varies by life stage. This helps bridge a gap in the literature between sustainability metrics and subjective well-being, highlighting that perceived benefits may not follow uniformly from technological or environmental performance indicators. This highlights that SC strategies should integrate environmental and human-centred design dimensions rather than relying solely on technological innovation to enhance residents’ quality of life. H3 was likewise supported, indicating significant age-based differences in perceived urban safety (p < 0.001). Together, these results demonstrate that perceptions of health, environment and safety within SC vary systematically across age groups, validating the study’s theoretical premise of intergenerational differentiation in smart urban experiences. It is important to note that the study design is cross-sectional and perception-based; therefore, the findings should be read as associations and group differences in perceived smart-urban experience rather than evidence that SC technologies directly cause well-being outcomes or generational disparities. However, even without causal claims, the pattern of group differences is theoretically informative: it suggests that the “promise” of smart urbanism is not experienced evenly and that demographic lenses are not merely descriptive add-ons but analytic tools for identifying where SC agendas may misalign with lived realities. Taken together, the age patterning supports the theoretical premise that “smart” urban experience is not a uniform outcome of technology deployment, but an interaction between infrastructures and residents’ perceived control, exposure and vulnerability across the life course.

When gender was introduced as an additional factor, the results revealed no significant multivariate main effect of gender across all dependent variables (Wilks’ Λ = 0.976, F(8, 458) = 1.38, p = 0.203), but several univariate trends emerged. Specifically, women reported slightly higher satisfaction with indicators of environmental comfort, whereas no significant gender-based differences were observed in perceived health, safety or air quality. A significant interaction effect between age and gender was detected only for Perceived age (p = 0.001), indicating that perceptions of one’s own aging differ across male and female participants depending on life stage. These findings suggest that gender moderates certain experiential aspects of smart urban living, albeit with small effect sizes and underscore the importance of integrating gender as an analytical dimension in future models of SC well-being. This also contributes to an unresolved issue in the literature: gender is often invoked as a vulnerability marker, yet empirical findings are frequently mixed or context-dependent (Austin et al., 2002; Foster and Giles-Corti, 2008). Our results support the view that gender effects may be subtle in aggregate but become more visible through interactions with life stage, suggesting that intersectional operationalizations may be more informative than single-axis comparisons (Pain, 2000; Valentine, 1989). Consistent with mixed evidence, the interaction suggests that gendered differences may become most visible when intersecting with age-linked roles, mobility patterns and exposure to urban risks.

A U-shaped distribution of subjective well-being was observed across age cohorts. Middle-aged participants reported the lowest levels of well-being (M = 3.63), while younger adults scored higher on self-rated health (M = 3.76), and older adults reported lower health levels overall (M = 3.17). These results are consistent with existing research indicating that subjective well-being tends to reach its lowest point during midlife, a period often associated with elevated stress, financial burdens and family responsibilities (Blanchflower and Oswald, 2008), yet also one that can involve greater stability and achievement, which may positively influence well-being (Diener et al., 2018). Yet the SC context invites a further interpretation beyond reproducing the U-shape pattern: midlife may coincide with heightened exposure to work–care pressures and time scarcity, which could make frictions in urban systems (mobility, noise, safety concerns and environmental stressors) more salient. This suggests that “smart” interventions that prioritize efficiency may still fail to translate into experienced well-being if they do not address the everyday constraints of particular life stages. Young adults reported better health outcomes, which aligns with the lower prevalence of chronic conditions and generally higher physical resilience in this age group (Jylhä, 2009). Older adults, while objectively facing more health challenges, often maintain a relatively positive self-assessment of their health due to adjusted expectations and psychological resilience (Steptoe et al., 2015) In a SC context, this implies that efficiency-oriented interventions may not translate into experienced well-being for midlife groups if everyday time pressures (work–care responsibilities) amplify sensitivity to environmental stressors such as noise or perceived insecurity.

Age also emerged as a significant factor in environmental perception. Older participants reported the highest levels of satisfaction with natural lighting (M = 4.51), air quality (M = 4.37) and quietness (M = 4.37), suggesting heightened sensitivity to sensory stimuli and a stronger preference for stable, comfortable living environments (Evans, 2003). Natural light is essential for maintaining circadian rhythm, sleep quality and psychological well-being (Figueiro et al., 2018), particularly among older adults, whose age-related visual changes can affect visual comfort and functional ability (Boyce, 2014). In contrast, younger adults appear more adaptable to artificial lighting conditions and more resilient to sleep disturbances and visual discomfort (Chellappa et al., 2011). Air quality also has particularly significant implications for older adults, who are more susceptible to respiratory illnesses (Brunekreef and Holgate, 2002; Kelly and Fussell, 2015). Lower mobility and increased time spent indoors further elevate the importance of clean air for this demographic (Wargocki and Wyon, 2017). Younger residents, on the other hand, tend to be more mobile and better able to adjust to environments with fluctuating air quality. Within SC debates, this supports the argument that “environmental smartness” is not only about monitoring and optimization but about designing perceptible and useable environmental comfort, particularly for groups whose daily routines make them more dependent on local, proximate conditions (Ahvenniemi et al., 2017; Albino et al., 2015; Ullah et al., 2024).

Perceived safety also varied across generational groups. Middle-aged participants reported the highest sense of safety (M = 4.41), followed by older adults (M = 4.32), while the lowest ratings were expressed by younger participants (M = 3.57). This distribution reflects differing life priorities: individuals in midlife often emphasize safety due to concerns related to family, housing stability and stronger involvement in local communities (Pain, 2000; Perkins and Taylor, 1996; Skogan and Maxfield, 1981). Younger individuals typically perceive a lower need for safety and exhibit greater tolerance for urban risks. In contrast, older adults, despite increased physical vulnerability, often maintain a relatively strong sense of safety through behavioural adaptations and spatial awareness (Foster and Giles-Corti, 2008; Lagrange and Ferraro, 1989). At the same time, the SC literature cautions that safety technologies can reconfigure public space through surveillance and behavioural regulation. Our findings, therefore, invite further research on whether age differences in perceived safety correlate with differences in trust, privacy concerns and perceived legitimacy of data-driven governance – an unresolved issue that cannot be settled with the present design but is sharpened by the observed group patterns. More broadly, the results reinforce that SC performance should be evaluated through differentiated lived outcomes, not only technical deployment.

To gain a more precise understanding of these differences, a post-hoc analysis using Tukey’s HSD test was conducted. This reinforces a key point of debate: the effectiveness of SC infrastructures cannot be assessed solely through deployment or technical performance but must be evaluated through differentiated experiences and perceived outcomes. Considering these differences, it is essential that the concept of the SC extends beyond technological efficiency and incorporates a human-centred, generation-responsive and participatory planning framework (Wang and Zhou, 2023). This includes the integration of human-in-the-loop approaches, which ensure that AI systems and smart infrastructure operate not in isolation, but in active collaboration with users, taking into account their age, lived experience and specific needs. Such an approach enables the development of adaptive, equitable and inclusive urban ecosystems that strengthen the partnership between humans and technology and foster conditions for sustainable well-being across the entire life course. Importantly, our results suggest that “human-centred” should be understood not as a generic design principle but as a demographic- and context-sensitive one: the same intervention may be experienced as enabling, irrelevant or burdensome depending on life stage and gendered patterns of mobility, care and exposure.

The observed intergenerational differences resonate with broader theoretical frameworks concerning age and technology adoption. Older adults tend to experience lower levels of digital efficacy and are often less exposed to smart services, resulting in weaker alignment between their needs and the functionalities of SC infrastructure (Czaja et al., 2006; Venkatesh et al., 2012). In contrast, younger groups may interact more naturally with digital systems but also report heightened sensitivity to environmental stressors, suggesting different priorities in urban well-being. This complicates a common binary in the literature that equates youth with advantage in smart environments: digitally fluent groups may still report lower perceived safety or environmental satisfaction, indicating that technical capability does not guarantee perceived well-being benefits.

From an environmental psychology perspective, these findings illustrate how perceptions of urban safety and environmental quality are mediated by generational identity and perceived control. Urban design elements that afford comfort, visibility and predictability may support subjective well-being more strongly in older populations.

While this study focuses primarily on age as a differentiating factor in SC perception, it should not be interpreted as suggesting that age alone accounts for differences in health, safety or environmental satisfaction. Instead, age serves as a heuristic entry point into the broader inquiry of how life course, technological adaptation and urban infrastructure interact. The complexity of urban experience cannot be captured by age alone; rather, it intersects with socioeconomic status, gender, education and digital literacy. Future studies should employ multivariate and mixed-method approaches to build a more comprehensive model of how SCs are perceived and inhabited by different population groups. Such work is also needed to address unresolved debates about how algorithmic governance, surveillance practices and participatory mechanisms shape trust and well-being across demographic groups (Butot et al., 2023; Choi and Kenney, 2024; Ristvej et al., 2020). These debates are central to contemporary SC scholarship, which increasingly recognizes that smart infrastructures may generate uneven perceived benefits and differential experiences of legitimacy and inclusion (Buttazzoni et al., 2020; Neirotti et al., 2014), yet remain only indirectly approached in our present design.

In this sense, our contribution is incremental: it provides context-sensitive empirical signals that can inform age- and gender-responsive planning, rather than prescribing a universal SC model. Policy-wise, the results support the argument that SCs must move beyond technological determinism towards inclusive design. This entails providing technological infrastructure and ensuring interpretive accessibility, designing cities that can be meaningfully used and trusted across generations. Digital literacy programs, participatory planning and age-specific environmental interventions are key tools to reduce the generational smartness gap.

At a time when the world is facing multi-level crises, ranging from accelerating planetary warming and the accumulation of toxic pollutants to digital waste and public health emergencies, the vulnerability of urban environments is increasing (Grum and Kobal Grum, 2023b). Within this context, SC can no longer be founded solely on technological efficiency; rather, they must evolve into human-centred ecosystems that integrate the perceptions, needs and expectations of diverse social groups.

This study analysed intergenerational differences in self-rated health and satisfaction with environmental and safety-related factors in self-identified SC. The results reveal a U-shaped pattern of subjective well-being, with middle-aged participants reporting the lowest levels of satisfaction. This aligns with previous research linking midlife to increased work-related stress, financial pressure and family responsibilities (Blanchflower and Oswald, 2008; Diener et al., 2018). Findings also indicate that older residents place significantly greater value on environmental factors such as air quality, natural lighting and acoustic comfort, consistent with studies highlighting increased sensitivity to sensory and health-related stressors in later life (Boyce, 2014; Evans, 2003; Figueiro et al., 2018). The importance attributed to air quality further supports existing evidence of older adults’ vulnerability to respiratory diseases and the negative health impacts of prolonged exposure to polluted air (Brunekreef and Holgate, 2002; Kelly and Fussell, 2015; Wargocki and Wyon, 2017). In contrast, younger individuals appear more adaptable, demonstrating higher tolerance for environmental fluctuations and greater mobility (Chellappa et al., 2011). Perceived safety – a key component of quality of life – was highest among middle-aged residents, which may reflect factors such as homeownership, family care responsibilities and stronger engagement in local communities (Pain, 2000; Perkins and Taylor, 1996). These findings call for urban and technological policy frameworks that reconceptualize SCs as adaptive, equitable and intergenerationally responsive systems. This includes the integration of human-in-the-loop decision-making models. Only by embedding user experience into the design and governance of urban systems can technology effectively support, rather than replace, human agency.

SCs of the future must not exist merely as technological constructs, but as dynamic, user-driven environments that recognize the diversity of needs across the life course and support long-term urban resilience, health and well-being. The integration of human-centred design principles, generation-specific strategies and community participation will be essential to ensuring that urban environments are capable of responding not only to current but also to future environmental, health and demographic challenges.

It is important to emphasize that this study does not claim that SC technologies directly cause generational differences in well-being or environmental satisfaction. Rather, our findings highlight how such differences become visible within urban environments labelled as smart by their residents. This descriptive lens is especially relevant in light of the subjective and self-identified nature of SC residence, which may reflect both technological infrastructure and cultural perceptions.

The findings offer several practical implications for urban planners, policymakers and SC stakeholders. Firstly, strategies aimed at increasing urban safety and environmental satisfaction should be differentiated across age groups, with targeted investments in noise reduction, air quality improvement and natural light access for older populations. For example, cities can prioritize “quiet routes” and low-noise zones around senior services and housing, use sensor-informed traffic calming where noise hot spots coincide with older residents’ daily paths and integrate lighting design standards that improve visibility without increasing glare (a frequent barrier for older adults). Secondly, smart infrastructure design should consider life-course differences in technological engagement, ensuring that systems remain inclusive, transparent and accessible to users with varying degrees of digital literacy. Practically, this means offering parallel access channels (digital and non-digital), publishing plain-language explanations of how data-driven services operate and providing assisted onboarding through libraries, community centres or primary care points. Finally, public communication about SC initiatives must be adapted to address the expectations and needs of different generational groups, strengthening participation, trust and awareness. One actionable step is to implement age-tailored participation formats (e.g. short mobile-friendly feedback for younger adults, facilitated workshops for older residents) and to report back publicly on “what changed” in response to resident input, which is a low-cost mechanism to strengthen perceived legitimacy. These implications call for interdisciplinary collaboration between urban designers, health experts and ICT developers to co-create environments that are not only technologically advanced but also socially equitable.

Future research can build on our results by addressing three specific questions. (1) Mechanisms: To what extent are age- and gender-patterned differences in perceived well-being mediated by digital inclusion (e.g. access, efficacy, trust) versus by non-digital contextual factors (e.g. housing conditions, daily mobility, neighbourhood cohesion)? (2) Governance and legitimacy: When do safety-oriented smart infrastructures increase perceived protection, and when do they increase perceived surveillance, and how do these trade-offs vary by life stage and gender? (3) Transferability: Do the same patterns replicate across cities with different governance models, service maturity and socio-cultural expectations of “smartness”? Methodologically, we therefore recommend (1) pairing subjective evaluations with objective indicators, (2) using longitudinal or repeated cross-sectional designs to test whether perceived benefits shift as SC services mature and (3) adding qualitative components to capture how different groups interpret “smart” services in everyday life. Cross-city comparisons within Central Europe (e.g. Ljubljana–Vienna–Prague) would be particularly informative for institutional comparability, while targeted contrasts with high-maturity SC contexts (e.g. Singapore, Seoul) could test boundary conditions for generalization.

In terms of bridging theory and practice, our results support socio-technical accounts of SC in which mobility, governance and environmental technologies function as interdependent levers of sustainable urban resilience. Rather than treating smartness as an intrinsic public good, the findings suggest that perceived benefits are uneven across demographic groups; therefore, policy frameworks should evaluate SC performance using human-centred outcomes alongside technical benchmarks.

The findings of this study underscore the necessity of redefining SCs as not merely technological systems but as socio-technical ecosystems that must accommodate diverse and intersecting dimensions of identity and experience. While age emerged as a significant differentiator of well-being, environmental satisfaction and perceived safety, it is crucial to recognize that age does not operate in isolation. Gender, in particular, represents a critical dimension of urban experience that intersects with age in shaping access to smart services, perceptions of safety and patterns of mobility. Women, especially older women, face distinct challenges in urban environments, including higher fear of crime, restricted nighttime mobility and differential access to digital technologies (Pain, 2000; Valentine, 1989). An actionable implication is that safety interventions should combine “hard” infrastructure (lighting, visibility and wayfinding) with “soft” governance measures, such as transparent rules for data collection, clear complaint channels and participation formats that reduce barriers for women and older adults. Future iterations of this research should adopt an intersectional lens that examines how age, gender, socioeconomic status and digital literacy jointly construct residents' experiences of smartness. Only by integrating these multiple axes of difference can SC planning move toward truly inclusive, equitable and generation-responsive urban futures.

The practical implications of this study suggest that policymakers and urban planners should embed principles of social equity and environmental justice into digital innovation frameworks. By adopting age-responsive planning, transparent governance and inclusive participatory mechanisms, cities can foster both perceived and actual safety, trust and overall well-being. Concretely, municipalities can (1) add a small set of well-being indicators (perceived safety, environmental comfort, perceived health) to SC dashboards, (2) disaggregate these indicators by age and gender and (3) require that major SC projects include an “inclusivity impact check” documenting accessibility, non-digital alternatives and feedback mechanisms. Importantly, incorporating psychological well-being indicators into SC performance metrics would enable a shift from purely technological benchmarks to human-centred outcomes.

At a societal level, the study contributes to a more realistic understanding of SC as intergenerational environments where perceived safety, environmental comfort and well-being are not evenly distributed. Making these differences visible can support more informed public debate, reduce “one-size-fits-all” narratives and strengthen trust by showing that SC agendas are evaluated through lived outcomes and not only through technological deployment.

This study has several methodological limitations that constrain interpretation and generalizability. Firstly, the non-probability sampling strategy and mixed online/offline recruitment may introduce selection and coverage bias (e.g. over-representing respondents with higher education, stronger social-network reach or higher digital engagement). Consequently, the findings should not be read as prevalence estimates for Ljubljana but as comparative patterns within a self-selected sample. Secondly, the study is restricted to a single urban context and to residents who self-identify their environment as an SC, which strengthens conceptual focus but increases contextual specificity and may omit residents who experience the same infrastructures differently or do not recognize them as “smart.” Thirdly, the design is cross-sectional and perception-based, so observed differences are associations; unmeasured confounders may partly account for group differences. Finally, reliance on self-report measures raises the possibility of common-method variance and social desirability effects. Taken together, these constraints imply that our conclusions should be treated as context-sensitive evidence that motivates replication with probabilistic sampling, richer covariates and longitudinal or mixed-method designs.

This study has several limitations. Firstly, while participants were asked about their living conditions and health, we did not systematically collect data on education level or household income, which could have functioned as control variables in a more complex multivariate model. As such, the interpretations of generational differences should be treated cautiously, acknowledging that socio-economic status may partially account for observed patterns. Secondly, while age served as a proxy for generational position, we did not directly assess attitudes toward or usage of smart technologies. Consequently, it remains unclear whether the observed differences are due to life stage or to differing experiences and expectations regarding SC technologies. Future studies should address these factors through longitudinal and mixed-method designs to better capture causal relationships and generational technology engagement.

Although gender was included as an additional factor in the extended analysis, its effects were modest, suggesting that more nuanced, intersectional approaches will be required in future research to capture the complex interplay between gender, age and urban experience.

Given well-documented gender differences in the perception of public space, safety and digital engagement (Pain, 2000; Valentine, 1989; Hilbert, 2011), future research should build upon these preliminary findings by adopting a fully intersectional framework that integrates multiple identity dimensions into both sampling and analytical design. Such an approach would enhance validity, equity sensitivity and explanatory depth in understanding SC well-being.

BG: Conceptualization, funding acquisition, investigation, resources, supervision, visualization, writing – original draft, writing – review and editing, formal analysis, methodology and validation. DKG: Data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing – review and editing and conceptualization.

The authors gratefully acknowledge the participants for their valuable time and engagement in this research. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Ethical approval was reviewed by the Ethics Committee of European Faculty of Law, New University, which confirmed that formal institutional approval was not required, as the research involved only anonymous questionnaires and no collection of personally identifiable data, in line with institutional ethical guidelines. The authors also acknowledge the journal’s request for transparency regarding the use of AI-assisted tools. Limited AI-based assistance (OpenAI’s ChatGPT) was used exclusively to support linguistic refinement, structural harmonization and terminology consistency during the manuscript revision process. No content was AI-generated; all conceptual, methodological, analytical and interpretative work remains entirely the authors’ own.