Smart but Unlivable? Rethinking Smart City Rankings Through Livability and Urban Sustainability: A Comparative Perspective Between Athens and Zurich

Abstract

While the ‘smart city’ concept is central to urban innovation, promising enhanced efficiency and livability, this paper interrogates a critical paradox: can cities be ‘smart’ yet ‘unlivable’? Existing indices, such as the IMD Smart City Index and the IESE Cities in Motion Index, while standard references, tend to prioritize technological and economic metrics, potentially failing to fully capture urban quality of life and sustainability. This study presents a preliminary attempt, based on an analysis of scientific literature, to critically examine current smart city indicators and propose a set of alternative indicators more representative of quality of life (QoL) and livability. The objective is not to overturn the rankings of cities like Zurich (high-ranking) and Athens (low-ranking), but to explore how a livability-focused approach, using more representative QoL indicators, might narrow the perceived gap between them, thereby highlighting diverse dimensions of urban performance. This research critically evaluates current smart city rankings. It aims to determine if livability-based indicators, supported by scientific literature, can provide a more balanced view of urban performance. This paper details how these alternative indicators were chosen, justifying their relevance to QoL with scientific support, and maps them to established smart city verticals (Smart Mobility, Smart Environment, Smart Governance, Smart Living, Smart People, Smart Economy). Finally, it outlines future research directions to further develop and validate this human-centric approach.

1. Introduction

The concept of “smart cities” has gained significant traction among urban planners and policymakers, promising to enhance urban efficiency and innovation through the integration of advanced technologies and digital governance. Traditional indices, such as the IMD Smart City Index (SCI) and the IESE Cities in Motion Index (CIMI), serve as benchmarks for measuring a city’s ‘smartness,’ typically focusing on aspects such as technological infrastructure, economic performance, and governance mechanisms [1,2]. Zurich is a prime example: it has ranked #1 globally in the IMD SCI in recent years (2023 and 2025) and near the top in IESE’s index, reflecting its advanced digital infrastructure and governance model [3]. In contrast, Athens consistently sits near the bottom of these rankings. This stark disparity in rankings underscores a critical paradox that motivates our study: Can a city be “smart” yet fail to provide a high quality of life for its residents? In other words, are current smart city rankings overlooking key livability and sustainability factors—leading to “smart” but potentially unlivable cities?

Smart city rankings often miss the mark on true urban quality of life, focusing instead on tech and economics with less emphasis on social or environmental well-being. The IMD SCI, for example, is criticized for its limited resident survey and data transparency [4]. The IESE CIMI combines over 100 indicators but uses opaque weighting with a strong corporate-economic bias, giving limited attention to social equity and environmental justice [2]. Critics argue that such tech-centric rankings can paint a skewed narrative of urban success, failing to reflect whether residents are healthy, happy, and secure [5]. Recent reviews of smart city assessment tools echo this concern: Patrão et al. (2020) find that many rankings neglect human-centric factors like community well-being and sustainability [1]. Similarly, Correia (2023) emphasizes that citizen engagement and quality of life need to be integral in evaluating smart cities [4]. In short, there is a research gap between the technology-focused criteria of prevailing smart city indices and the broader livability outcomes that determine whether a city is truly “smart” for its people [6].

This study proposes to bridge that gap by developing an alternative set of livability and sustainability indicators for smart cities and examining how the picture of urban performance changes when these indicators are applied [7]. We focus on a comparative case study of Athens and Zurich—two cities that epitomize the divergence between conventional smart rankings and on-the-ground livability. Rather than aiming to overturn the rankings of a top-performer (Zurich) and a lower-ranked city (Athens), our goal is to explore how a livability-focused assessment might narrow the perceived gap between them [8,9]. We ask: if we evaluate Athens and Zurich on dimensions like public health, affordable housing, social inclusion, and climate resilience—alongside technology—what new insights emerge about their urban performance?

This work proposes a holistic framework for assessing urban development by integrating quality of life metrics into the smart city discourse. We identify key livability indicators (e.g., life expectancy, green space access, civic engagement, housing affordability, urban heat resilience) based on urban sustainability literature, justifying each with scientific and institutional references, and mapping them to established smart city dimensions [10,11,12]. Our Athens–Zurich comparison shows how a human-centric approach reveals overlooked strengths and weaknesses (see Figure 1).

This has implications for policymakers and ranking bodies: integrating livability metrics can create more balanced evaluations and smarter urban strategies that prioritize well-being alongside technology. Ultimately, this study redefines “smartness” to include sustainability and livability, ensuring smart cities are advanced and truly livable for all [9]. Zurich and Athens were selected as representative examples of contrasting performance in global smart city rankings. Zurich consistently appears at the top of indices such as the IMD Smart City Index (SCI) and the IESE Cities in Motion Index (CIMI), while Athens often ranks among the lowest. The IMD SCI is based on the perceptions of 100–120 residents per city across five thematic areas (health, safety, mobility, activities, and governance), while the IESE CIMI compiles more than 100 indicators across 10 dimensions using secondary data. Both indices, however, have been criticized for their bias toward technological and economic performance and for underrepresenting social and environmental well-being. The aim of this research is to critically evaluate existing smart city rankings and to test whether livability-based indicators—grounded in scientific literature—can offer a more balanced representation of urban performance. We hypothesize that applying these alternative indicators will narrow the perceived performance gap between high-ranked cities (e.g., Zurich) and lower-ranked ones (e.g., Athens), thus revealing dimensions of smartness that are often overlooked. The remainder of this paper is structured as follows. Section 2 outlines the methodology, including the indicator selection process and the data sources. Section 3 presents comparative results across the nine livability indicators. Section 4 discusses the policy implications and limitations of this approach. Section 5 concludes with a summary of findings and future research directions.

Literature Review

We reviewed over 40 peer-reviewed articles published between 2015 and 2024 from journals such as Sustainability, ISPRS IJGI, Cities, and Urban Studies. The review focused on smart city assessment tools, livability frameworks, and the integration of social, environmental, and technological indicators. Key findings reveal that most rankings prioritize ICT infrastructure and economic performance, often neglecting human-centric dimensions such as mental health, housing affordability, and civic engagement. This gap motivates our effort to define an alternative, livability-focused assessment framework. The spatial distribution of urban functions and services significantly impacts a city’s smartness and livability, with disparities in access to essential amenities profoundly influencing the quality of life for residents, particularly in underserved areas. Research indicates that uneven distributions can adversely affect healthcare, recreational spaces, and retail access, ultimately lowering overall urban satisfaction. For instance, studies have shown significant disparities in the availability of public open spaces in various cities, which highlight how certain neighborhoods lack adequate access to green areas, essential for residents’ physical and mental well-being. A similar concern has been highlighted by other research, emphasizing that accessibility to green spaces plays a critical role in determining citizens’ well-being, underscoring the urgent need for equitable distribution of such amenities [13].

With increasing recognition of these disparities, researchers are beginning to incorporate fine-grained spatial analysis into smart city assessments to better evaluate urban environments. For example, Chen et al. proposed a visual-language reasoning model (LaRSE) that classifies building footprints, allowing for a more precise understanding and management of urban functions across large metropolitan areas. Additionally, Guo et al. introduced a deep learning-based method for urban land-use segmentation, enhancing the ability to analyze and visualize spatial distributions and their impacts on urban livability [14]. Such advancements denote a shift towards data-driven solutions aimed at addressing the equitable distribution of urban services, which remains a critical gap in prevailing smart city frameworks that often lack detailed spatial–functional analyses [2,15].

Moreover, the integration of urban planning paradigms that account for the accessibility and functionality of services can significantly influence urban sustainability. This includes a diverse range of services, such as hospitals, schools, parks, and retail nodes, which are vital for maintaining a high quality of urban life. Research suggests that accessibility to well-distributed services can promote social equity and enhance the livability of urban areas, which is crucial for contemporary urban governance. Studies support the notion that incorporating these dimensions into smart city assessments can facilitate better-informed decisions, leading to genuine improvements in urban livability, advocating for frameworks that prioritize not only technological advancements but also the equitable spatial arrangement of urban services [14,15].

This dimension remains largely absent in mainstream smart city indices and deserves further integration.

2. Materials and Methods

2.1. Research Design and Approach

We adopted a mixed-methods comparative case study approach focusing on Athens and Zurich as illustrative examples of divergent smart city rankings. The research comprised two main stages: (1) a literature-driven selection of livability indicators, and (2) a data collection and analysis comparing those indicators between the two cities. This structure aligns with standard IMRaD methodology, ensuring clarity in how indicators were chosen and how the comparison was conducted.

In stage one, we conducted an extensive review of academic literature, policy documents, and global urban datasets to identify candidate indicators that reflect urban livability and sustainability dimensions. We particularly targeted indicators that prior studies have linked to quality of life (QoL) but are underrepresented in major smart city indices. The guiding principle was to capture outcomes of a “smart city” in human terms—health, well-being, inclusion, and environmental quality—rather than just technological inputs. To structure our selection, we drew on the well-established six smart city dimensions (also known as verticals): Smart Economy, Smart People, Smart Governance, Smart Mobility, Smart Environment, and Smart Living [16]. These dimensions (first popularized by Giffinger et al. and others) provide a holistic categorization of smart city goals, beyond ICT alone. We aimed to map each livability indicator to one or more of these dimensions, thereby demonstrating that our proposed metrics integrate into—and expand—the existing smart city framework (see Figure 2).

Based on the literature review, we selected a set of eight livability indicators that emerged as crucial for QoL in urban contexts: (1) Life Expectancy at Birth, (2) Prevalence of Stress-Related Illnesses (mental health), (3) Air Quality Index (AQI), (4) Urban Noise Pollution levels, (5) Average Commute Time & Transport Modal Split, (6) Housing Affordability (Rent-to-Income ratio), (7) Public Green Space per Capita (accessible), and (8) Social Cohesion & Civic Engagement. We also included (9) Urban Heat Vulnerability & Climate Resilience as a combined indicator of climate-related livability. Each indicator was chosen for its documented relevance to residents’ well-being and its connection to sustainable urban development, as supported by prior research:

Life Expectancy: A fundamental health outcome measure reflecting cumulative living conditions and healthcare quality. Justification: Life expectancy at birth is widely recognized by the World Health Organization (WHO) and others as a key indicator of public health and overall living standards. It encapsulates the long-term impacts of education, income, environment, and healthcare access on a population [17,18]. Mapping: Primarily Smart Living, with links to Smart People (human capital).

Stress-Related Illnesses: The prevalence of mental health issues (e.g., chronic stress, anxiety, depression) in the urban population. Justification: Mental well-being is critical to QoL but often overlooked by techno-centric city evaluations. High stress levels can result from urban factors like job pressure, congestion, noise, and insecurity, directly reducing life satisfaction and productivity. Scientific support: Urban epidemiological studies show city living can elevate risk of mental disorders; measuring stress can involve surveys like the Perceived Stress Scale or health records [19,20]. Mapping: Smart Living (health aspect) and Smart People (human capital and social support).

Air Quality Index (AQI): Level of air pollution (particulates, NO₂;, etc.) typically reported as an index. Justification: Clean air is fundamental to urban livability; pollution contributes to respiratory and cardiovascular diseases, shortening life spans and lowering daily comfort. Support: Standard AQI metrics are recommended by WHO and environmental agencies, and numerous studies link chronic exposure to poor air with adverse health outcomes and reduced QoL [21,22]. Mapping: Smart Environment (primary) and Smart Living.

Urban Noise Pollution: Usually measured by indicators like day–evening–night sound levels (L_den) above certain thresholds (e.g., >55 dB). Justification: Excessive noise (traffic, construction) causes stress, sleep disturbance, and even cardiovascular effects, impacting mental and physical health. It is a frequently cited complaint in cities, reflecting environmental quality. Support: WHO guidelines and EU Environmental Noise Directive highlight noise as a critical urban health factor; cities track L_den and L_night levels to gauge exposure. Mapping: Smart Environment and Smart Living.

Commute Time & Modal Split: Average one-way commute duration, and the distribution of transport modes (e.g., percentage of trips via public transit vs. private car, walking, cycling). Justification: Daily mobility affects residents’ time use, stress, and access to opportunities. Long commutes erode work–life balance and are associated with lower life satisfaction. Modal split indicates sustainability and healthiness of transport—higher public transit, walking, or cycling shares are linked to cleaner air, less traffic stress, and more physical activity. Support: Studies show that each additional minute of commute can decrease subjective well-being, and cities with integrated, efficient transport tend to report higher QoL [23]. Mapping: Smart Mobility (primary), plus Smart Environment (emissions) and Smart Living (convenience).

Housing Affordability: Typically measured as the ratio of housing costs (rent or mortgage) to household income, or the percentage of households spending above a threshold (e.g., >40% income on housing). Justification: Affordable shelter is a basic human need; when housing costs consume excessive income, residents face financial stress and may sacrifice other necessities, harming overall well-being. Housing affordability also influences social stability and economic productivity (e.g., workers’ ability to live near jobs). Support: Housing cost burden is widely used by institutions like the U.S. Department of Housing and Urban Development (HUD) and OECD. Research confirms that a high rent-to-income ratio correlates with lower life satisfaction. A recent study found that housing cost burden significantly predicts self-reported life satisfaction declines. (Some scholars argue for complementary measures like residual income after housing, but the rent/income ratio remains a straightforward indicator) [24,25]. Mapping: Smart Living (housing quality of life) and Smart Economy (economic opportunity and cost of living).

Public Green Space per Capita: The amount of accessible public green area (parks, urban forests, recreation areas) per resident, often in square meters per person. We also consider % of city area that is green or tree-covered as a proxy. Justification: Access to green spaces is crucial for recreation, physical and mental health (reducing stress and improving mood), and provides ecosystem services like cooling and pollution filtration. Green spaces encourage exercise and social interaction, supporting community cohesion. Support: The WHO recommends that all residents have a sizable green space (0.5–1 hectare) within 300 m of their home. Numerous studies link higher green space availability to better health outcomes and higher perceived QoL. For example, Addas (2023) demonstrated positive impacts of urban green coverage on life satisfaction [20,26]. Mapping: Smart Environment (primary) and Smart Living (health/recreation).

Social Cohesion & Civic Engagement: This is a qualitative indicator reflecting the strength of social bonds, community trust, and citizen participation in civic activities (e.g., voting rates, volunteering, community initiatives). Justification: A “smart” city ultimately depends on its people. Social cohesion and active civic life enhance resilience, safety, and good governance. High social capital is associated with lower crime, better public health outcomes, and more responsive institutions. Engaged citizens are more likely to support and co-create smart solutions that address local needs. Support: Social cohesion can be measured through surveys (e.g., Neighborhood Cohesion Index, Civic Engagement Index) and by metrics like voter turnout or mutual social support rates. For instance, Teo and Chum (2020) showed that stronger neighborhood cohesion correlates with better mental health in communities [27]. The OECD Better Life Initiative also tracks social support (percentage of people who feel they have someone to rely on) as an important well-being metric. Mapping: Smart People (social capital) and Smart Governance (citizen participation), with ties to Smart Living.

Urban Heat Vulnerability & Climate Resilience: This composite indicator assesses how well the city is prepared to handle extreme heat and climate change impacts, including the intensity of the urban heat island (UHI) effect and the adaptation measures in place. Justification: With climate change, heat waves have become a major threat to livability, especially in cities with dense built environments. High UHI intensity (cities several degrees hotter than surroundings) can cause discomfort, health risks (heat stroke, mortality among vulnerable groups), and strain on energy (air conditioning). A truly “smart” city must safeguard its residents through climate resilience planning (cooling infrastructure, greening, emergency response). Support: Urban heat vulnerability is typically assessed by combining data on exposure (temperature, lack of vegetation), sensitivity (e.g., elderly population, prevalence of chronic illness), and adaptive capacity (infrastructure, health services). Studies document methods to map heat risk and correlate them with heat-related mortality. Mirzaei et al. (2020) for example model UHI mitigation strategies in cities [28]. Both Athens and Zurich have published climate resilience or heat action plans, indicating the city-level engagement with this issue. Mapping: Smart Environment (climate adaptation), Smart Living (health safety), and Smart Governance (strategic planning).

After defining and justifying these indicators, we mapped each indicator to the six smart city dimensions to ensure integration with the smart city concept [16]. Figure 1 below summarizes this mapping, showing that our proposed indicators are not “outside” the smart city framework but rather enrich each vertical with livability-focused metrics.

Many indicators cross-cut multiple dimensions (for example, Housing Affordability primarily relates to Smart Living, but also influences Smart Economy in terms of talent retention and economic equity). This mapping exercise follows recommendations in the literature to align livability with smart city planning, thereby helping city officials translate QoL objectives into specific strategy areas (mobility, environment, governance, etc.). To operationalize this mapping, we followed a literature-informed yet expert-driven approach. The research team (as domain experts) assigned each livability indicator to the smart city dimension(s) that best fit its scope, guided by established framework definitions—particularly the six-pillar model proposed by Giffinger et al. [16]. We did not conduct an external stakeholder workshop or formal validation for this conceptual mapping stage; instead, we ensured consistency with widely used smart city categories to maintain conceptual soundness. This approach is an initial step. Future work could include validation with external experts or city stakeholders (e.g., through Delphi panels or participatory workshops), as suggested by Patel & Patel [16].

We selected these nine indicators based on three criteria: relevance to quality of life (QoL) supported by scientific literature, availability of comparable data for both cities, and alignment with the six smart city dimensions. Indicators such as education quality, crime/safety, and income inequality were excluded due to lack of reliable, city-level data for both Athens and Zurich. These dimensions are acknowledged as important and suggested for future studies with broader datasets. In this preliminary framework, we applied equal weighting to each indicator for comparability. However, we acknowledge the need for future work to apply weighting schemes—such as Analytic Hierarchy Process (AHP), Delphi surveys, or citizen-weighted indices—to better reflect stakeholder priorities.

2.2. Data Sources and Validation

In stage two, we gathered data for each selected indicator for Athens and Zurich, aiming for the most recent available figures (circa 2020–2025) and ensuring comparability. We prioritized open-access and reputable sources including: national statistical agencies (e.g., Hellenic Statistical Authority ELSTAT for Greece, Swiss Federal Statistical Office), international organizations (WHO, OECD, Eurostat), and peer-reviewed studies or reports. Where city-specific data were not readily available, we used regional or national data as proxies with caution, noting the limitations. For example, life expectancy was available at national level and for the Attica region (which includes Athens) and was used to approximate the city’s value.

2.3. Study Context: Athens and Zurich

Athens and Zurich were selected for their stark contrast in smart global city rankings. To evaluate their livability, we compiled and compared data across nine indicators.

Table 1. Comparative data and key insights Athens vs. Zurich.

Proposed Indicator	Athens	Zurich	Brief Comparative Analysis/Insight
Life Expectancy at Birth (Overall)	Attica Region (2023): 81.7 years. National (Greece, 2024 est.): 81.9 years.	National (Switzerland, 2023): ~83.8 years. National (Switzerland, 2021 WHO): 83.3 years.	Zurich (Switzerland) shows a higher national life expectancy than Athens (Attica/Greece), reflecting overall better-performing healthcare systems.
Prevalence of Stress-Related Illnesses	Greek studies indicate high stress/anxiety/depression. General adult population: 10.8% depression, 12% anxiety. Blueground Work–Life Balance Index: 77 (lower rank).	Swiss studies: 28.2% of employees experience job stress (2022); 15% of the population with moderate/severe mental stress. Blueground Work–Life Balance Index: 91.8 (higher rank).	Available data suggests significant stress levels in both cities. Zurich scores higher on a work–life balance index, but a direct city-level prevalence comparison is challenging with current data.
Air Quality Index (AQI)	Annual avg. 2023: 39 AQI (“Good”).	Annual avg. 2023: 22 AQI (“Good”).	Both cities generally show “Good” AQI, with Zurich consistently reporting lower (better) numerical values.
Noise Pollution (Lden > 55 dB/Lnight > 50 dB)	A 2018 study revealed 52% of the population was exposed to daytime levels of 65–70 dB. Greece has not submitted data to the EEA for over a decade.	Road traffic is the main source of noise. One in seven people in Switzerland is exposed to excessive noise. Specific city data requires deeper extraction.	Athens has concerning data on noise exposure, well above EU/WHO thresholds. For Zurich, while the issue is recognized nationally, comparable city-level data is lacking.
Average Commute Time (one way)	~20 min (national average, 2019).	~30.1 min for work commuters (national average, 2023).	The average commute time appears to be lower in Greece than in Switzerland, contradicting the idea that “smarter” cities are necessarily more efficient in daily transport.
Transport Modal Split (% Public Transport)	~37% Public Transport (PT); another source: PT 33%, Cars 39%.	Zurich Metro Area: 32% public transport mode share. City: PT 39%.	Zurich appears to have a comparable or slightly higher public transport modal share compared to Athens.
Housing Affordability (Rent-to-Income Ratio)	Greece: Highest housing cost overburden rate in EU cities (27.3% in 2022). Numbeo Athens: ratio ~59%.	Numbeo Zurich: 27 years of average gross salary needed to buy a 70 m2 apartment.	Athens faces severe housing affordability challenges. Zurich, while expensive, presents a paradox of extreme unaffordability despite higher incomes.
Public Green Space per Capita (Accessible)	Variable and low estimates: 6.63 m2/person or less.	Official city guideline: 8 m2/inhabitant. Managed green and forest areas make up 43% of the municipal area, suggesting high per capita availability.	Zurich appears to have significantly more green space per capita. Athens has a chronic shortage.
Social Cohesion & Civic Engagement (Qualitative)	Strong tradition of civic participation, active local initiatives (SynAthina). OECD: 78% rely on someone in need; voter turnout 57% (2019).	High ranking in Intercultural Cities Index; strong tradition of public participation. Voter turnout 46.66% (2023).	Both cities show evidence of civic engagement. Athens demonstrates community resilience despite challenges. Zurich has strong formal structures for participation.
Urban Heat Vulnerability & Climate Resilience	High vulnerability; UHI up to 10 °C. Resilience Strategy & Heat Action Plan active. C40 City.	Swiss urban areas are vulnerable to heat. Zurich Climate Resilience Alliance active. C40 City.	Both cities acknowledge heat vulnerability and are developing resilience strategies. Athens faces acute and well-documented heat challenges.

summarizes the values used, data sources, and brief interpretation.

Key sources used for each indicator were as follows:

Life Expectancy: National life expectancy at birth for Switzerland and Greece in 2023 was obtained from country health data (World Bank/OECD estimates consolidated on countryeconomy.com). Switzerland’s average life expectancy is about 84.3 years (86.0 female, 82.4 male) [29], whereas Greece’s is about 81.8 years (84.4 female, 79.2 male) [30]. Additionally, we referenced the Attica region data (Athens’ region) which was ~81.7 years, similar to the national figure. These data are consistent with UN and WHO statistics, underscoring reliability.

Mental Health (Stress-Related Illnesses): Comparable city-level mental health prevalence is difficult to obtain; instead, we compiled indicators from academic surveys and national health reports. For Athens/Greece, epidemiological studies report that around 10–12% of the adult population suffer from depression or anxiety disorders [31] (figures aligning with WHO depression prevalence estimates for Europe). For Zurich/Switzerland, surveys indicate roughly 15% of the population experiences moderate to severe stress, and about 28% of employees report significant job stress [32]. We also cited an international “work–life balance index” by a global firm (Blueground), on which Athens scored 77/100 and Zurich 91.8/100, suggesting Zurich’s workforce perceives better work–life balance on average [33]. While these figures come from different sources, together they suggest both cities face notable stress and mental health burdens, with Zurich possibly having an edge in mitigating work stress (likely due to higher social support and services). All data points were cross-checked with sources such as WHO mental health databases and national surveys for plausibility.

Air Quality (AQI): We retrieved real-time and annual average AQI data from open city air monitoring platforms and the IQAir database (which aggregates stations globally). In 2024, both Athens and Zurich typically registered “Good” AQI levels on average. Athens’ annual average AQI was around 35–40 (indicative of PM2.5 in the low-teens µg/m³). Zurich’s air is slightly cleaner, with AQI often in the 20–25 range. These data came from national environment agencies and IQAir’s 2023 city report, and we cite a snapshot indicating Athens AQI ~38 vs. Zurich ~23 [34]. Both cities benefit from regulatory efforts (e.g., EU emission standards), though occasional pollution spikes (Athen’s summer smog, Zurich winter inversions) occur. We ensured the AQI scales were consistent (US EPA AQI scale) for a valid comparison.

Noise Pollution: Detailed city-wide noise exposure data (percent of population exposed to >55 dB, etc.) were extracted from the European Environment Agency (EEA) noise maps and reports. The EEA identifies road traffic as the dominant noise source in both cities [35]. According to the latest EEA noise mapping, large portions of Athens’ population are exposed to high road noise, especially along major arteries, and similarly for Zurich (though Zurich has more stringent noise abatement). We note that specific L_den/L_night values for each city required data mining from EEA’s database; our qualitative analysis simply acknowledges both Athens and Zurich face urban noise challenges, consistent with any major city. For reliability, we referenced EEA’s 2020 report showing that in many European capitals, 20–30% of residents are exposed to road noise above 55 dB. This aligns with our general statements [36].

Commute Time & Modal Split: We utilized transportation surveys and reports from each city. Athens’ average one-way commute was about 30 min (for an average distance ~10 km), which is in line with surveys by the Hellenic Institute of Transport [37]. Zurich’s average commute is also around 30 min (nationally, Swiss workers average 30.1 min). Thus, commute durations are comparable between the two, highlighting that time alone might not differentiate them strongly. However, the modal split provides more insight: In Athens, roughly 33–37% of commuters use public transport, with the remainder mostly in private cars (around 39% by one estimate) [38]. Zurich has a well-developed transit network; in the city proper, public transit accounts for ~39% of trips, and about 32% in the greater metro area [39]. We obtained these figures from municipal reports and an OECD urban mobility dataset. All sources were recent (2019–2022) and we cited them where relevant. To ensure comparability, we looked at pre-pandemic patterns to avoid anomalies due to COVID-19. While methodologies differ slightly (Athens’ figure includes all trip purposes; Zurich often reports work commute modal split), both clearly show that public transport carries roughly one-third of trips in each city, with Zurich perhaps slightly higher.

Housing Affordability: We drew on EU and international housing statistics. A striking data point comes from Eurostat’s urban indicators: Athens (and Greek cities generally) have the highest housing cost overburden rates in Europe. In the mid-2010s, about 40% of Greek urban households spent over 40% of their income on housing—by far the highest in the EU (recent figures remain high, though improved to ~33% by 2020) [40]. In addition, according to Numbeo’s 2025 data, the average monthly net salary in Athens is around €980, while average rent for a one-bedroom apartment is ~€580, yielding a rent-to-income ratio of ~59% [41]. This confirms the severe affordability problem (households often rely on multiple earners or informal support). For Zurich, although rents are very high (e.g., around CHF 1650 for a one-bed apartment), incomes are also high; Swiss government guidelines typically advise housing costs not exceed ~25–33% of income. The average rent-to-income ratio in Zurich comes out much lower than Athens—roughly in the 20–30% range for a median household, based on local salary data [42,43,44]. To summarize reliably: Athens faces a critical housing affordability crisis, whereas Zurich, despite expensive housing, has less relative burden due to higher incomes [24].

Green Space per Capita: For Athens, we combined multiple sources. One measure from the European Environment Agency (EEA) indicates that only about 15% of Athens’ municipal area is public green space accessible to the public [45]. Another study (LSE research) found approximately 6.6 m² of green space per person in Athens—one of the lowest figures among European capitals. A recent open-access study noted Athens has only 0.96 m² of green space per person in public areas, ranking it fourth from the bottom among European cities [46]. By contrast, Zurich is known for its abundant greenery. According to the City of Zurich’s records (Grün Stadt Zürich), about 43% of the municipality’s area is green (parks and forests). With a population of ~436,000, this equates to roughly 90 m² per person]. Conservatively, Zurich has tens of square meters of green space per resident—far above Athens. To maintain consistency, we emphasize qualitative comparison: Athens has a severe green space deficit—trees cover only ~10% of the city [47] and accessible park area is well under 10 m² per capita [46]—whereas Zurich offers ample green spaces, with over a third of the city under green cover and dozens of square meters per capita. Data were verified against OECD environmental indicators and local urban plans. We note that slight discrepancies in exact values exist across sources (due to definitions of “green space”), but all sources concur on the magnitude of difference.

Social Cohesion & Civic Engagement: We used both qualitative descriptions and quantitative proxies. For Athens, we documented the presence of strong grassroots initiatives and community networks, like, for example, the SynAthina platform which coordinates local volunteer groups, and various civic tech tools (like Novoville) to engage citizens. These illustrate a culture of community resilience in the face of economic challenges. Quantitatively, the OECD How’s Life? report provides two relevant metrics for Greece: only 78% of people in Greece report having someone to rely on in times of need, versus the OECD average of 91% [41]; and voter turnout in recent national elections was about 58% in Greece, below the OECD average ~69%. We interpret these as lower-than-average social capital, likely reflecting recent hardships (economic crisis impacts trust and participation). For Zurich/Switzerland, social trust and engagement are traditionally high: Switzerland often has high volunteering rates (over 40% of the population volunteers), and very strong direct democratic participation (voter turnout varies by election but civic involvement in local decisions is extensive). Zurich scores highly on the Council of Europe’s Intercultural Cities Index, indicating active policies for social inclusion. However, interestingly, recent surveys in Switzerland have flagged concerns that social cohesion may be weakening (e.g., societal divisions over issues like migration) [48]. We present a balanced view: Both Athens and Zurich demonstrate robust civic engagement, but in different forms. Athens’ citizens have shown resilience through informal support networks and local initiatives, whereas Zurich benefits from institutionalized civic participation and social stability.

Urban Heat & Climate Resilience: We compiled climate data and policy documents. Athens is notorious for intense summer heat—the urban heat island effect can raise city-center temperatures by up to 8–10 °C above surrounding areas. We referenced climate studies and NASA satellite data to confirm UHI intensity. Athens has responded by developing a Resilience Strategy and a Heat Action Plan (as part of its membership in the C40 Cities network), which include planting trees, creating cooling centers, and “Cool Roofs” programs. We note Athens appointed a Chief Heat Officer—a first in Europe—indicating how acute the issue is. Zurich, while in a temperate climate, is not immune to heatwaves; Swiss cities recorded extreme temperatures (~37 °C) in recent summers. Zurich has joined initiatives like the Zurich Climate Resilience Alliance and is also a C40 city committed to climate action. Zurich’s smart city strategy explicitly incorporates environmental sustainability and climate adaptation (e.g., using data from an urban sensor network to monitor microclimates). We used C40 reports and each city’s climate adaptation plan to source these points. Both cities being part of C40 is a testament to their recognition of climate risks. We ensure credibility by citing official C40 documentation of membership and summarizing each city’s status (Athens facing acute heat challenges, Zurich actively planning but less severe historically) [28,49].

All data were validated by checking for consistency across multiple sources. For instance, life expectancy and housing burden figures were cross-verified with WHO and Eurostat, respectively. Where data quality was a concern (e.g., private index like Blueground’s), we treated it as supplementary insight and not a sole source. Throughout the analysis, we explicitly acknowledge when data are approximate or when direct city-to-city comparisons are limited by differing metrics. This transparent approach addresses potential methodological limitations and was noted by peer reviewers as essential for reliability.

Finally, to synthesize the comparative findings, we constructed a summary table (Table 1) listing Athens vs. Zurich on each indicator, along with sources for each datapoint and a brief comparative insight. This table, adapted and condensed from our data compilation, serves as the basis for the Section 3 narrative. Every figure or claim in the Results is traceable to a cited source as detailed above. In cases where our interpretation or extrapolation was required (e.g., inferring city-level stress from national data), we have indicated this in the text to maintain transparency.

3. Results

3.1. Rethinking “Smart”: Athens vs. Zurich Through Livability Indicators

We compared Athens and Zurich across the selected livability and sustainability indicators, revealing a more nuanced picture of each city’s performance than their smart city rankings alone convey. Table 1 provides a snapshot of the comparative data and key insights, and the following subsections elaborate on these findings.

Life Expectancy: In terms of basic health outcomes, Zurich outperforms Athens. The life expectancy at birth in Switzerland (nationally) is about 84.3 years [29], one of the highest in the world, whereas in Greece it is about 81.8 years [30]—a few years lower. Regional data for Attica (the Athens region) likewise hovers around 81–82 years. This gap reflects long-term differences in healthcare systems, living conditions, and socio-economic stability. Zurich’s residents benefit from a high-quality healthcare network and preventative services, contributing to longevity. Indeed, Zurich and Switzerland consistently rank near the top in health indicators globally. Athens, while not far behind in absolute terms (Greek life expectancy is still above the EU average of ~80 years), faces challenges such as economic austerity impacts on health services and lifestyle risk factors. The implication is that a city can be “smart” on paper but still have room to improve in fundamental health outcomes—a dimension often underweighted in tech-centric indices. We note that neither the IMD nor IESE index explicitly includes life expectancy as a criterion. Our findings suggest it should be considered, as it encapsulates many underlying facets of livability (environmental quality, healthcare, etc.).

Mental Health and Stress: Both cities show signs of modern urban stress, albeit data are not directly comparable. In Athens, ongoing economic strains and rapid urbanization have contributed to significant levels of stress-related illness. Recent studies estimate that approximately 11% of Greeks suffer from depression and a similar proportion from anxiety disorders. Especially in Athens, youth and certain occupational groups (e.g., healthcare workers) report elevated stress and burnout rates. On the other hand, Zurich’s high-pressure professional environment can also produce stress: around 28% of Swiss employees reported high work-related stress in 2023. However, Zurich’s culture of work–life balance and strong social safety nets may mitigate everyday stress to a degree. For example, a global index of work–life balance gave Zurich a score of 91.8 vs. Athens’ 77 [30], suggesting Zurich offers more supportive conditions (e.g., flexible work, social support) compared to Athens. Overall, both Athens and Zurich recognize mental well-being as a priority—Athens has introduced community mental health programs post-crisis, and Zurich has proactive workplace wellness initiatives. The presence of stress-related health issues in both a top-ranked smart city and a lower-ranked one underscores that human well-being is a separate axis of performance, not automatically solved by technological advancement. A city’s “smart” strategy must incorporate mental health (e.g., through smart healthcare, community building) to be truly livable.

3.2. Environmental Quality: Air and Noise

Air Quality: Fortunately, neither Athens nor Zurich suffers from extremely poor air by global megacity standards; both often enjoy “good” air days. In 2023, Athens’ annual average AQI was ~38 (on a scale where <50 is good), reflecting fine particulate (PM_2.5) concentrations generally in the low-to-mid teens µg/m³ [50]. Zurich’s air quality was even better, with an average AQI in the 20s, corresponding to PM_2.5 around 6–8 µg/m³—meeting WHO’s recommended air quality guidelines on many days [51]. These differences are partly geographic (Athens’ basin can trap smog during summer heatwaves, and Saharan dust occasionally impacts it, whereas Zurich benefits from alpine winds) and partly policy-driven (stringent Swiss emissions controls). On high-pollution days, Athens does experience smog (notably from traffic and sometimes winter heating or wildfires), with short-term AQI spikes into “moderate” or worse ranges, something Zurich rarely encounters. Comparatively, Zurich’s advantage in air quality aligns with it being a wealthier city that has invested in clean public transit and emissions regulation—an aspect reflected in Smart Environment initiatives. Athens has made strides (e.g., phasing out older diesel cars on bad days), but its challenges with congestion and older vehicles remain. This indicator shows that smart city rankings could incorporate air quality as a core metric: it directly affects health and correlates with innovative environmental policies (e.g., low-emission zones are a “smart” intervention). The case of Athens vs. Zurich demonstrates how a city can leverage technology (monitoring stations, pollution control tech) and policy (regulations) to improve air—something current smart indices might only indirectly capture.

Noise Pollution: Urban noise is a less publicized but impactful livability factor. According to the C, road traffic is the main noise source in both Athens and Zurich. While precise data on population exposure require detailed mapping, indicative comparisons can be made. In Athens, the dense urban fabric and heavy vehicle traffic mean many neighborhoods experience day–night average sound levels exceeding WHO guidelines (>55 dB day, >50 dB night). Anecdotally, Athens is a noisy city—from honking cars to bustling nightlife—and lacks widespread noise insulation. Zurich, by contrast, though also a busy city, enforces strict noise ordinances (for example, night-time delivery and construction are tightly regulated) and benefits from greater spatial planning (green buffers, etc.). Still, main roads in Zurich see levels above 55 dB affecting thousands of residents [52]. Both cities have hotspots of noise (e.g., Athens’ arterial roads and tourist districts; Zurich’s airport flight path and busy tram lines). The insight here is that noise is a ubiquitous urban problem, not clearly reflected in smart rankings. Our analysis finds both Athens and Zurich likely have significant portions of their population exposed to high noise, which can detract from QoL [18]. This is a call for smart city frameworks to consider the acoustic environment—possibly using smart sensors for noise mapping and mitigation (which some cities are starting to do). In absence of a numeric “winner” (due to data gaps), we stress that addressing noise through smart urban design (e.g., traffic calming, sound barriers, green zones) is a component of a truly livable smart city.

3.3. Mobility and Transport Lifestyles

Commute Times: Despite very different urban layouts, Athens and Zurich residents spend a similar amount of time commuting on average. Both hover around a 30 min one-way commute for workers [23]. This suggests that Athens’ traffic congestion, while notorious at times, is somewhat offset by relatively shorter distances or perhaps lower car ownership among some residents, whereas Zurich’s efficient transit and compact city help keep commute times reasonable despite high ridership. It is interesting that Athens, often perceived as having chaotic traffic, still manages an average commute in line with Switzerland—possibly because many Athenians live in the central city and commute shorter distances (or because some avoid traveling due to unemployment or remote work). Zurich’s consistency (the Swiss nationwide average is 30.1 min) reflects well-planned transport networks. From a livability perspective, a half-hour commute is moderate; anything significantly above that tends to diminish life quality. Neither city appears to have the extreme commutes seen in larger metros (e.g., 60+ min common in megacities). Thus, in terms of time, Athens and Zurich are comparable. However, smart city indices like IMD do consider commute or mobility, but often via qualitative surveys. Our data-driven approach confirms a similarity that might be missed if one assumed the lower-ranked city (Athens) automatically has worse commuting.

Transport Modal Split: A more revealing contrast emerges in how people commute. Athens relies heavily on private cars, although it has a decent public transit share. Approximately 33–37% of Athens commuters use public transport (bus, metro), and roughly 39% use personal cars (with the remainder walking or other modes). This indicates a roughly balanced split between cars and transit for those with jobs. Athens’ metro expansion in the 2000s improved transit share, but car use remains high due to urban sprawl in Attica and cultural preferences. Zurich, in the city proper, achieves about 39% public transport mode share (including its extensive tram, bus, and S-Bahn network), with much of the rest split between cars, walking, and cycling. Notably, Zurich has a high cycling rate (by some estimates ~7–10% of trips) and walking (25%+), whereas Athens’ cycling share is negligible and walking is moderate. Thus, Zurich’s mobility is more multi-modal and sustainable, reflecting a “smart mobility” paradigm of an integrated, efficient system. The Swiss also have one of the highest per capita public transit usage rates in the world. Implication: When evaluating smartness, one should credit cities that enable greener transport choices. Our findings show Zurich’s transit and non-motorized transport usage slightly surpass Athens’, aligning with its smart city reputation in mobility. Yet Athens is not as far behind as some might think; its public transit share is actually comparable, which is an encouraging sign. The difference is qualitative: Athens struggles with overcrowded buses and some missing links, whereas Zurich’s system is comfortable and deeply integrated (single ticket system, timetable synchronicity—hallmarks of a smart system). This nuance underscores that smart mobility is not just about having an app or autonomous vehicles, but about achieving high usage of efficient modes.

3.4. Housing and Cost of Living

Housing Affordability: This indicator starkly highlights a livability challenge often obscured in tech-focused rankings. Athens faces a severe housing affordability crisis. In the latest EU statistics, around 33% of Greek urban residents are in “housing cost overburden,” meaning they spend over 40% of their disposable income on housing costs—the highest rate in Europe [40]. As noted, the average rent for a modest apartment in Athens can consume well over half of an average salary. This is unsustainable for livability, leading to overcrowding (young adults staying with family) and in worst cases, homelessness. By contrast, Zurich is one of the most expensive housing markets globally, yet median incomes are also very high. The city’s housing cost to income ratios, while not trivial, are typically around 25–30% for many households—within or slightly above recommended limits. For example, a 1-bedroom apartment rent (~CHF 1650) is roughly 30% of a median monthly household income in Zurich. Additionally, Switzerland provides housing assistance and has cooperative housing schemes that alleviate burdens. Thus, Athens’ residents experience far greater relative housing stress than Zurich’s. Notably, this kind of socio-economic indicator is often missing from smart city indices; yet it critically affects quality of life and a city’s ability to attract and retain talent (a “smart economy” concern). Our comparison suggests that if rankings considered housing affordability, Athens’ situation would flag a major weakness in its urban sustainability, while Zurich’s performance (though facing affordability issues of its own, especially for middle-class families) would still be markedly better. Urban planners in Athens are increasingly aware of this, exploring affordable housing initiatives, whereas Zurich continues to manage supply through zoning and incentives for non-profit housing. This indicator exemplifies the need for integrating economic livability factors into the notion of smart cities—technology alone does not solve housing costs.

3.5. Urban Environment and SpaceHousing and Cost of Living

Green Space Access: Perhaps the most dramatic divergence between Athens and Zurich is in green infrastructure. Athens is green-space poor. According to European surveys, Athens offers as little as 2–3 m² of green space per resident in its dense core [46]. Even by more generous measures, it is around 6–7 m² per person citywide. Tree cover analysis corroborates this: only ~10% of Athens’ urban area has tree canopy [47], far below European averages (~30%). The city’s few large parks (e.g., the National Garden ~0.15 km², Pedion tou Areos ~0.27 km²) are undersized for a metropolis of ~3 million in the metro area. This lack of greenery contributes to heat retention and deprives residents of recreation and relief—a clear livability issue. In contrast, Zurich is a “green city.” In the municipality, official stats indicate 43% of the land is green (parks, woods, water). Major natural assets like the Zürichsee (Lake Zurich waterfront) and forests (Adlisberg, Uetliberg slopes) fall partly within city limits, giving residents ample access. Estimates of green space per capita in Zurich range widely depending on definitions, but are orders of magnitude higher than Athens—likely tens of m² per person in accessible parks alone, not to mention nearby forests. For instance, one could roughly calculate ~85–90 m²/person from the earlier data (though not all is equally accessible). Even focusing on parks within walking distance, Zurich’s residents generally have a park or garden in their neighborhood. The implication for smart city assessment is profound: environmental livability assets like green space are mostly absent from current smart rankings, yet they clearly differentiate cities in terms of sustainability and QoL. By highlighting green space, our comparative approach shows that Zurich’s smart city success is complemented by abundant green infrastructure, whereas Athens’ low smart ranking coincides with a deficiency in this area—possibly exacerbating its other problems like heat and stress. If a “smart living” dimension were to include green space per capita (as some urban sustainability indexes do), Athens would be flagged for urgent improvement. We observed that Athens is making efforts (e.g., small pocket parks, tree planting initiatives often spurred by NGOs), but structural constraints (dense build-out, lack of large public land reserves) limit rapid progress. Meanwhile, Zurich continues to invest in its Grünstadt program, expanding urban gardens and protecting green belts. This indicator also illustrates how smart technologies can play a role: e.g., sensors to monitor park usage, apps to encourage visitation, or IoT-based irrigation—but fundamentally, there is no substitute for the physical presence of nature in the city for livability.

Social Cohesion & Civic Engagement: Both Athens and Zurich demonstrate strengths in social capital, though manifested differently. Athens, despite economic hardships, has a vibrant civil society. Grassroots solidarity networks emerged strongly during the Greek economic crisis (community kitchens, time banks, etc.), and platforms like SynAthina (supported by the municipality) coordinate hundreds of citizen groups tackling urban issues. This indicates a culture of community resilience—residents stepping up to solve problems collaboratively (e.g., reclaiming public spaces, assisting vulnerable neighbors). Athens also has seen improvements in participatory governance; for example, the city’s “Develop Athens” program and digital apps allow reporting issues and feedback. However, some quantitative measures from OECD suggest room for improvement: only 78% of Greeks feel they have someone to rely on in need (low social support), versus 91% OECD average [51], and electoral participation is middling (e.g., 58% turnout), possibly reflecting disenchantment. Zurich, conversely, has long-standing high civic engagement in a formal sense—Switzerland’s direct democracy means citizens vote frequently on referenda (turnout for federal votes ranges ~45–60%) and engage in communal associations (sports clubs, neighborhood councils). Volunteerism in Switzerland is among the highest in Europe (~40% engage in some form of volunteering annually). Zurich also prides itself on social cohesion across its diverse population; it was highly rated in the Intercultural Cities Index for policies on integration. That said, surveys indicate Swiss people have growing concerns about social trust and inclusion, given changes like immigration and economic shifts. A recent study showed only about half of Swiss respondents felt social cohesion was “strong”—a decline from previous sentiment. In summary, both cities have engaged communities: Athens shows bottom-up engagement and solidarity [53], and Zurich exhibits structured participation and volunteerism. Neither smart city ranking fully captures this—though IESE’s index has a “social cohesion” category where interestingly Zurich scored well (7th), suggesting its strength was noted, but Athens’ grassroots strengths were perhaps overlooked due to lack of data. Our results underscore the value of including qualitative and survey-based indicators of community life in assessments. A smart city is not just about government services, but also about empowered, connected citizens (“smart people” dimension). Athens demonstrates that even a lower-tech city can have social innovation and engagement that enhance livability, an insight that rigid rankings might miss.

Climate Resilience (Urban Heat): Finally, comparing climate resilience highlights both cities’ recognition of global challenges, with Athens in a more immediately vulnerable position. Athens’ urban heat island effect and extreme summer temperatures (regularly exceeding 40 °C) pose health and infrastructure risks]. The city has responded by taking a leadership role in climate adaptation: it is one of the first to implement a dedicated heat action plan, with measures like cooling centers, greening initiatives, and public warning systems. Athens is also part of the C40 network’s urban climate resilience program [49]. These efforts are decidedly “smart” in that they use data (e.g., climate models, satellite imagery to identify hotspots) and innovative governance (e.g., Chief Heat Officer coordinating across sectors)—yet traditional smart city rankings did not account for such adaptation initiatives. Zurich faces milder climate impacts but is proactive as well: it has committed to net-zero emissions by 2040 and runs the “Resilient Zurich” program focusing on heat among other issues. Zurich’s UHI is less intense (maybe 3–5 °C at peak), but the city still experiences heatwaves and accordingly has expanded green roofs, shade in public spaces, and community cooling centers (especially after the 2003 Europe heatwave). Both cities are members of C40 Cities, signaling high-level commitment to climate action [28]. The key insight is that climate resilience is a critical component of urban sustainability and livability; a city overwhelmed by climate stress cannot be “smart” in practice, no matter its digital prowess. Our comparative view shows Athens excelling in recognition and initial action on this front (arguably more so than many higher-ranked smart cities who have yet to confront such crises), and Zurich integrating resilience into its smart city roadmap. We suggest that future city indices explicitly include climate adaptation and resilience metrics—perhaps rewarding cities like Athens that, despite fewer resources, innovate in addressing climate risks (e.g., Athens’ use of cool pavement or data-driven heat vulnerability mapping is noteworthy).

3.6. Synthesis: Livability Gaps and Convergences

Bringing these findings together, we observe that Zurich’s top smart city ranking is not contradicted by our livability assessment—indeed, Zurich performs strongly on most alternative indicators as well, which is reassuring. High-tech governance in Zurich coexists with high QoL: long life expectancy, ample green space, efficient transport, relative housing affordability (for a wealthy context), and active civic life. However, we also uncover areas where even Zurich has challenges, such as housing costs (absolute prices are high) and pockets of social cohesion concerns. This suggests that even leading smart cities must continuously address livability issues—a reminder that “smart” is not a static status.

For Athens, the analysis paints a more optimistic picture than its dismal smart city rank (e.g., IMD SCI 2023 placed Athens 113th globally). Despite technological lags, Athens exhibits strengths in community engagement and is making strides in sustainability (e.g., climate action). Some quantitative gaps remain stark—e.g., life expectancy ~3 years lower, green space ~10× lower, housing burden ~2× higher in Athens compared to Zurich. These are significant quality of life disadvantages that technology alone has not solved. Yet, Athens’ example also highlights that a lower-ranked city can innovate socially and environmentally in ways not captured by smart city metrics. The perceived gap between Athens and Zurich narrows when we consider these broader indicators: Athens may not catch up to Zurich’s digital infrastructure soon, but it can improve livability (and indeed must, to avoid losing population and investment). Conversely, Zurich can learn from Athens’ social resilience and ensure that its technological progress does not overlook vulnerable communities.

In summary, our results illustrate that smart city rankings can misrepresent or oversimplify urban sustainability and livability. When evaluated through our expanded set of indicators, the narrative of “Zurich vs. Athens” becomes more complex than a simple first-versus-last ranking. It becomes a story of two cities with different strengths and weaknesses: one excels in governance, economy, and environment, the other in social ingenuity and cultural richness—and both facing the universal urban challenges of health, inclusion, and climate. The findings advocate for a recalibrated view of “smartness”: technological smartness should be complemented by human-centric smartness, measured through livability outcomes.

Our comparison captures a snapshot but also hints at evolving trajectories: Athens is increasingly investing in climate resilience, appointing a Chief Heat Officer and expanding green areas, while Zurich faces rising housing costs and emerging concerns about social cohesion. These dynamic trends affirm the need for longitudinal monitoring in future evaluations.

4. Discussion

4.1. Implications for Smart City Rankings and Urban Policy

Our comparative analysis of Athens and Zurich through livability and sustainability metrics carries several implications for how smart city rankings are formulated and how cities strategize their development:

• Broadening the Smart City Paradigm: The results affirm that the definition of a smart city must transcend technology deployment and include quality of life outcomes. Early smart city paradigms often equated “smart” with wired infrastructure and data-driven management. However, as our study shows, factors like health, housing, environment, and social cohesion critically determine whether technological solutions translate into real public value. Smart city rankings should integrate livability indicators as core components, not as afterthoughts. Doing so would create a more balanced scorecard that cities can aim for. For instance, a revised index might weigh health outcomes (like life expectancy or pollution-related illness rates) and environmental conditions (green space, air quality) alongside ICT access and e-governance [4,11]. This responds directly to identified gaps in current indices, which have been criticized for neglecting sustainability and equity. If the IMD, IESE, or similar rankings were to evolve in this direction, cities like Athens might receive more credit for improvements in livability, and cities like Zurich would be encouraged to maintain not just their tech edge but also social and environmental excellence.
• Recognizing Multiple Pathways to “Smartness”: A crucial insight from Athens vs. Zurich is that there are different pathways for cities to become smarter and more livable. Zurich exemplifies a top-down, resource-rich approach: heavy investments in technology, strong institutions, and planned sustainability (e.g., systematic transit expansion, housing policies, etc.). Athens, with far fewer resources, has seen bottom-up innovations: community initiatives, adaptive reuse of spaces, and leveraging its cultural capital. Smart city frameworks should acknowledge such context-driven approaches. Rather than a one-size-fits-all yardstick where many developing or crisis-hit cities rank poorly, frameworks could include improvement indices or contextualized benchmarks. A city’s trajectory (how much it improves QoL over time given its starting point) might be as important as its absolute rank. This relativity is hinted at in Correia (2023), who suggests assessing cities based on their developmental phase in the smart journey. For policymakers, the lesson is that focusing on human-centric initiatives can yield dividends even if high-tech solutions are not immediately feasible [4,11]. Athens’ progress in areas like civic tech (citizen reporting apps) and climate adaptation, despite budget constraints, illustrates that political will and citizen engagement can drive smart outcomes. In other words, “smartness from below”—empowering communities—can complement “smartness from above” (infrastructure).
• Highlighting Overlooked Issues: By including indicators such as housing affordability and mental health, our study shines light on issues often overlooked in smart city dialogs. The case of housing is particularly notable: Zurich’s technology and prosperity do not automatically solve housing stress—a reminder that even leading smart cities can struggle with basic livability issues like affordability [48]. Meanwhile, Athens’ dire housing situation undermines its livability irrespective of any digital advancements. This suggests that smart city initiatives must be paired with social policies. A city investing in broadband and smart sensors should also invest in affordable housing strategies (e.g., incentivizing development of affordable units, or using smart data to identify housing needs). Similarly, the importance of green space and climate resilience in our comparison indicates that environmental well-being is a foundational pillar of urban smartness. A city cannot claim to be truly smart if its residents suffer from extreme heat or lack access to nature—consequences that also have economic and health costs. By acknowledging these indicators, city leaders and international frameworks can push for a more integrated approach where ICT projects are evaluated also for their contributions to livability (e.g., does a smart transportation system reduce commute stress and pollution? Does a smart grid help the city mitigate climate risks?). The alternative indicators act as a checklist ensuring that smart city strategies keep sight of human priorities.
• Rethinking Rankings as Tools, Not Goals: Another discussion point is the purpose of rankings themselves. Cities often chase higher rankings for prestige or to attract investment. Our findings caution that such pursuits, if narrowly focused (e.g., launching visible tech projects to boost rankings), might neglect deeper issues (see

  Table 2. Comparative overview of selected smart city rankings.

  Index	Organization	Key Dimensions	Methodology	Criticisms Highlighted
  IMD Smart City Index	IMD and SUTD	Health, Safety, Mobility, Opportunity, Governance	Based on surveys of ~100–120 residents per city	Limited sample size; perception-based data; lack of transparency; technocentric orientation.
  IESE Cities in Motion Index	IESE Business School	Economy, Human Capital, Environment, Connectivity, Governance	Combines 114 indicators from varied sources; weighting varies	Methodological opacity; arbitrary weights; strong economic and corporate bias; limited focus on social equity.
  Juniper Research Smart City Rankings	Juniper Research	Energy, Transport, Public Safety, Smart Infrastructure	Technology deployment-centric	Overemphasis on infrastructure and tech adoption; neglect of social and environmental dimensions; supply-side bias.
  U4SSC Key Performance Indicators	ITU (UN-led consortium)	Economy, Environment, Society and Culture, ICT	Based on ISO standards and SDGs; cities self-report data	Under development, limited global adoption; questions about comparability and implementation in Global South contexts.

  ). As an example, a city might implement flashy smart kiosks or AI systems (scoring points on an index) while affordable housing worsens—a scenario where the city becomes “smarter” on paper but less livable. This phenomenon has been criticized as “technological solutionism” in urban planning. The Athens and Zurich cases encourage reframing: rankings should be seen as diagnostic tools to guide comprehensive improvement, not end goals. For Athens, a low rank coupled with our livability analysis identifies specific domains for improvement (green space, housing) that might not be evident from the ranking alone. For Zurich, a high rank should not breed complacency; our analysis identifies remaining challenges (like ensuring housing affordability and sustaining social cohesion) that a pure tech focus might miss. Ultimately, the discussion should shift from “Who is number 1?” to “How can each city learn from others to improve QoL for its citizens?”. In that sense, the Athens–Zurich comparison is not about champion vs. underdog, but about peer learning: Athens can adopt certain best practices from Zurich (e.g., integrated transport policies, long-term housing strategies), and Zurich might draw inspiration from Athens in citizen-led urban interventions and cultural vibrancy.
• Policy Integration of Livability Metrics: We recommend that urban policymakers explicitly integrate livability metrics into their smart city roadmaps. This could mean setting targets like: increase public green space per capita by X% by year Y, or reduce average commute time by Z min, or ensure housing cost burden for median households stays below a threshold. By monitoring these alongside traditional smart indicators (Wi-Fi coverage, open data portals, etc.), city administrations can ensure a more balanced development [54]. The two case cities illustrate this well. Zurich’s smart city strategy already includes environmental sustainability—it could go further to include social goals (for example, Zurich could aim to reduce its rent burden disparity or to enhance social mixing in neighborhoods). Athens, which has many pressing social needs, could tie any smart city funding to projects that also improve livability (for instance, using smart tech to optimize water use for new green spaces, or to target energy subsidies to the most vulnerable during heatwaves) [24,40]. Crucially, international organizations and funders (like EU’s urban innovation programs) might consider conditioning support on such integrated approaches, to avoid tech-only “smart city” projects that do not yield QoL gains.
• Communication and Citizen Perception: Including livability in the notion of smart cities can also make the concept more tangible and acceptable to citizens. Residents care about clean air, safe streets, affordable homes—if they see the smart city agenda addressing these, public support will grow. In Athens, for instance, some skepticism exists about flashy smart city projects when basic services lag; focusing on QoL could help align the smart city initiative with what people need most. In Zurich, communicating how technology is being used to maintain their high quality of life (e.g., smart energy systems to keep air clean, or e-government to bolster democratic participation) can reinforce trust and engagement. Essentially, livability metrics provide a common language between city officials and citizens, grounding the often abstract concept of “smartness” in everyday experience [8,10].

4.2. Limitations

While our study provides valuable insights, it is not without limitations. First, the analysis was conducted on a comparative case study of just two cities. Athens and Zurich were chosen for their contrasting profiles, but generalizing findings to all cities should be done cautiously. Other cities might exhibit different relationships between smart initiatives and livability outcomes. Future research should apply our framework to a broader sample of cities—including medium-sized cities and those in developing countries—to verify the robustness of these indicators in diverse contexts.

Second, there were data limitations for certain indicators. City-level data on mental health, noise exposure, and even green space (in comparable terms) proved difficult to obtain. We occasionally relied on regional or national proxies, which might not fully capture the city situation (e.g., Swiss national stress levels vs. Zurich city stress). We also included a mix of hard statistics and softer qualitative assessments (e.g., “strong tradition of civic participation” in Zurich) which, while grounded in evidence, introduce some subjectivity. The precision of comparisons like “Athens has X m² green space vs. Zurich Y m²” is limited by differing data definitions. We mitigated this by cross-referencing multiple sources and focusing on orders-of-magnitude differences rather than fine differentials. Still, our findings should be interpreted as indicative rather than exact. Improved standardized data (for example, an international urban livability database) would greatly aid future analyses.

Another limitation is that our proposed indicators, though more holistic, are still not exhaustive. Urban livability has dimensions we did not cover in depth, such as education quality, safety/crime rates, cultural amenities, and income inequality. We chose indicators that were prominent in the literature and particularly relevant to the Athens–Zurich context (and to peer reviewer feedback), but a truly comprehensive assessment might include these additional factors. For instance, crime rates were not explicitly discussed—both Athens and Zurich are relatively safe (Zurich more so), but that is certainly a key QoL factor. Likewise, education (Smart People dimension) could be considered: Zurich’s highly educated workforce vs. Athens’ brain drain issue. These were outside our immediate scope but merit inclusion in future frameworks. We aimed for a feasible set of indicators; however, future research could expand the indicator set and even weight them to create a composite livability–smartness index.

We also note that causality cannot be firmly established in our study. We identified correlations and contrasts—e.g., Zurich has both high tech and high QoL; Athens low tech and some low QoL metrics—but we cannot claim that smart city initiatives (or lack thereof) caused those QoL outcomes. Many underlying variables (economic wealth, governance quality, geography) influence both technology adoption and livability. Our approach is primarily descriptive and exploratory. To strengthen causal inference, future studies might use longitudinal data (did QoL improve after implementing a certain smart intervention?) or statistically control for confounders across a large sample of cities.

Lastly, an important limitation is that our perspective implicitly values certain aspects of livability that might involve normative judgments. For example, we treat more green spaces as unequivocally positive (which is generally supported by research, but one could argue there are trade-offs with density). We assume longer life expectancy is a universal good (fair, though it could be affected by lifestyle choices). While we buttressed each indicator with literature and global benchmarks (like WHO guidelines), it is worth recognizing that the ideal balance of indicators can vary by cultural context. For instance, some societies might prioritize cultural vibrancy or innovation over, say, commute time. We attempted to select universally relevant indicators, but cultural bias in defining livability is a risk. Engaging citizens in defining what livability means to them would be a valuable step for city-specific implementations of this framework.

4.3. Future Research Directions

This study opens several avenues for future inquiry:

Broader Empirical Validation: As mentioned, applying our livability-oriented framework to a larger set of cities would test its general applicability. Researchers could conduct a quantitative analysis across dozens of cities, correlating traditional smart city scores with various QoL indicators. It would be illuminating to see, for example, if high-ranked smart cities systematically have better air quality, or if some low-ranked cities outperform on social cohesion. Such analysis could potentially yield a revised index or at least identify common outliers (cities that are “smart but unlivable” or “livable but not tech-smart”) for deeper case studies.

Indicator Refinement and Weighting: Future work might refine the list of indicators and even develop a composite Livability-Adjusted Smart City Index. This would involve engaging experts and stakeholders to weight indicators according to importance (perhaps using methods like Analytic Hierarchy Process or expert surveys). For instance, should life expectancy weigh more than commute time? How to quantify social cohesion for ranking purposes? There is a methodological challenge in merging subjective and objective indicators, but efforts like the OECD Better Life Index provide precedents where citizens can weight aspects as they see fit. A participatory approach to weighting could reflect what people value in a “smart & livable city”. Additionally, more robust metrics could be introduced: e.g., Healthy Life Expectancy (to focus on quality of health, not just length of life) or Gini coefficient for income inequality (as part of Smart Economy/Social inclusion).

Impact Evaluation of Smart Initiatives on QoL: A critical next step is to research causal links: do smart city projects tangibly improve these livability indicators? For example, if Athens invests in a smart traffic management system, does average commute time or air quality improve? If Zurich rolls out a new e-governance platform, does civic engagement increase or stress decrease (by reducing bureaucratic hassle)? Rigorous program evaluations (difference-in-differences, randomized trials if feasible) of specific interventions would help justify the inclusion of certain indicators in smart city planning. Over time, as more cities pursue “smart” projects, a body of evidence can be built on what works for QoL and what does not. Our framework could guide such studies by focusing attention on outcomes rather than outputs.

Incorporating Subjective Well-being: We focused on objective or at least observable indicators, but another complementary direction is to incorporate subjective well-being measures—how residents rate their own quality of life. Surveys like Gallup World Poll or city-specific happiness indices could be used. It would be interesting to see if residents of high-tech cities are actually “happier” or more satisfied than those in less wired cities, when controlling for income, etc. If not, that would further support our thesis that tech alone is not delivering what matters. Conversely, if a city like Athens had surprisingly high subjective well-being (some studies note strong social ties can buoy happiness despite hardships), that too should be considered part of being “smart” in the sense of fostering a fulfilling life. Future smart city rankings might even incorporate life satisfaction scores (some national indices do).

Policy Case Studies: Further qualitative case studies could delve into how cities can transition to a livability-focused smart agenda. For example, a follow-up study could document Athens’ efforts to integrate the indicators we discussed into its urban planning (perhaps via its new smart city strategy office). Similarly, examining how Zurich maintains balance as it grows (ensuring, say, that tech growth does not exacerbate inequality) could yield best practices. Knowledge-sharing between cities—possibly through networks like Eurocities or C40—could be facilitated by framing around these indicators. Research can support this by compiling case studies of initiatives: e.g., “City X increased green space per capita by 20% using smart land-use planning—what can others learn?” or “City Y implemented an affordable housing data dashboard that helped reduce rent burden.” These practical insights help move from theory (having indicators) to action (improving those indicators).

Longitudinal Monitoring: Finally, it would be valuable to turn this framework into a monitoring tool. Cities could be encouraged to publish an annual “smart livability report” tracking these indicators. Researchers and city statisticians could collaborate to refine data collection. Over years, one could track whether, say, Athens’ life expectancy catches up, or Zurich’s social cohesion improves, and attribute these changes to specific policies. Monitoring would also reveal any unintended consequences (does focusing on one indicator lead to neglect of another?). Essentially, future research could operationalize our framework into a dashboard for city managers, followed by studying its adoption and efficacy in policymaking.

5. Conclusions

This study set out to rethink smart city rankings by foregrounding livability and sustainability indicators, and our comparative analysis of Athens and Zurich demonstrates the value of this approach. We found that incorporating health, environmental, social, and housing metrics provides a much richer narrative of urban performance than conventional tech-centric indices. Zurich, the perennial smart city leader, indeed shows strong livability outcomes in many areas—but not all, reminding us that even top-ranked cities have their unaddressed challenges. Athens, often labeled a smart city laggard, exhibits critical strengths and efforts that a narrow focus on technology would overlook. In short, we illustrated that a city can be highly “smart” yet face livability issues (the smart-but-unlivable risk), and conversely a city can improve quality of life even without being a tech leader. The convergence of smart city and livability agendas is not only possible but necessary for the next evolution of urban development.

For urban practitioners and ranking institutions, the message is clear: adopt a more holistic, human-centric lens in evaluating and planning smart cities. A recalibrated framework—where digital innovation and human well-being are dual pillars—will encourage cities to pursue strategies that genuinely enhance residents’ lives. After all, the ultimate promise of a smart city is not just to implement cutting-edge technology, but to create an environment where people can thrive. Our comparative perspective between Athens and Zurich serves as a compelling case study in this regard, but it is just one step. We hope it spurs further action: updated ranking methodologies by organizations like IMD, integrated policy goals by city governments, and continued scholarly research on the intersection of smart city and sustainable livability.

Limitations and Future Work: We acknowledge that our research is exploratory and focused on two cases with data constraints. The findings should be verified across more cities and refined with better data and additional indicators (education, safety, inequality, etc.). Despite these limitations, the evidence presented strongly supports the principle that livability must be front-and-center in smart city discourse. Future research was outlined to expand and deepen this investigation, including broader empirical studies and impact evaluations of smart policies on QoL.

In conclusion, the adage “what gets measured, gets managed” applies: if smart city success is measured only by technology metrics, cities will manage those and possibly miss out on actual well-being. By measuring and comparing cities on livability indicators alongside tech metrics, we can better manage—and inspire—the transition to cities that are both smart and sustainable, innovative and inclusive, high-tech and high-QoL. Cities like Zurich and Athens, each in their own way, are writing this next chapter. It is time for our ranking tools and urban strategies to catch up.