Infrastructure Resilience in Hyper-Arid fast-growing Cities: Public Perceptions, Institutional Confidence, and AI Trust in Dubai

Infrastructure Resilience in Hyper-Arid fast-growing Cities: Public Perceptions, Institutional Confidence, and AI Trust in Dubai | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Infrastructure Resilience in Hyper-Arid fast-growing Cities: Public Perceptions, Institutional Confidence, and AI Trust in Dubai Hassan Alblooshi, Mohamed Mustafa, Yacine Rezgui, Thomas Beach This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8490604/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urban resilience in rapidly growing cities requires more than technical infrastruc- ture solutions, it depends on public awareness, institutional trust, and acceptance of emerging technologies. Dubai provides a critical case where climate stress, demographic diversity, and a Smart City agenda converge, a reality underscored by the April 2024 extreme rainfall event that caused widespread flooding and transport disruptions across the city. This study examines resilience perceptions in Dubai, integrating hazard awareness, preparedness, institutional confidence, and trust in AI-enabled systems. Results show that visible hazards such as floods and traffic disruptions dominate risk perception, while slower-onset threats like groundwater depletion remain underestimated. Preparedness is strongly corre- lated with institutional confidence but is significantly lower among expatriates. While AI-driven resilience solutions attract support, trust in these technologies is conditional and hinges on oversight, transparency, and inclusivity. The findings highlight three interlinked vulnerabilities, fragmented hazard perception, uneven preparedness, and fragile trust in institutions and technologies. From these, five principles are proposed to guide resilience strategies, broaden hazard awareness, make preparedness mandatory, embed transparency in governance, establish safe- guards for AI, and ensure inclusivity across demographic groups. Beyond Gulf studies that emphasize infrastructure or governance capacity, this study provides empirical evidence on how demographic divides, institutional trust, and attitudes toward emerging technologies shape resilience in Dubai. Beginning with a public perception survey as the first stage of a broader mixed methods design, it offers a foundation that will inform subsequent Delphi consultations and resilience planning in hyper arid, fast-growing cities across the Gulf and wider Global South. Infrastructure resilience Preparedness Institutional confidence AI trust Dubai Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Urban resilience has become a defining concern of the twenty-first century. As cities expand at unprecedented rates, they face mounting risks from climate change, environ- mental degradation, and complex interdependencies across infrastructure systems. The United Nations Office for Disaster Risk Reduction (UNDRR) and the Sendai Frame- work for Disaster Risk Reduction (2015–2030) emphasise that enhancing resilience requires both technical capacity and social engagement, including risk awareness, pub- lic trust, and inclusive governance (UNDRR, 2015 ; UNDRR, 2022). The Sustainable Development Goals (SDG 11) further reinforce the need for cities to be “inclusive, safe, resilient, and sustainable,” highlighting the importance of linking infrastructure performance with public preparedness and institutional accountability (UNHabitat, 2023). Arid and hyper-arid urban regions are especially exposed to compound risks (IPCC 2022 ; UNHabitat, 2023). In Dubai, natural aquifer recharge is minimal, leaving only 3% of groundwater fresh and forcing reliance on desalination and aquifer storage (Gonzalez et al. 2016 ). Demand pressures are intensifying, with electricity consump- tion reaching 56.5 TWh in 2023, a 6.3% annual increase driven by cooling needs (Saberi-Derakhtenjani et al. 2024 ), while peak summer temperatures may exceed 50°C, compounding stress on infrastructure (Park et al. 2022 ). Rapid urban expansion has further strained ecosystems and governance capacity (UNDESA, 2022; (Awad and Jung, 2022 ). Environmental stresses, including rising air pollution (Akasha et al. 2024 ), add to systemic vulnerabilities. Acute shocks also intersect with these chronic pres- sures: major floods in 2024 disrupted transport and caused insured losses of USD 2.9–3.4 billion (Francis et al. 2025 ; (Allam et al. 2024 ). These overlapping challenges underline that resilience in Dubai cannot be achieved through infrastructure capacity alone, but is also conditioned by how residents perceive risks, prepare for disruptions, and trust institutional and technological responses. Dubai provides a critical case for examining these dynamics. The city’s population has grown more than tenfold since the 1970s, with urban expansion rates exceeding 10% annually in the early 2000s (Nassar et al. 2014 ). Today, it is home to over 3.6 million people, projected to rise to 5.8 million by 2040 (Government of Dubai, 2022 ). More than 80% of residents are expatriates (GLMM, 2018), making them central to the city’s resilience. Yet they remain underrepresented in formal governance and policy- making. Their perceptions matter not only because they constitute the overwhelming majority of residents, but also because gaps in preparedness, awareness, or institutional confidence can undermine the effective implementation of resilience strategies. Capturing these perceptions is therefore essential. Globally, resilience research has shown that frameworks often remain conceptual and struggle to connect theory with practice (Meerow and Stults, 2016 ; Anguelovski et al. 2016 ). In the Global South, Western-centric models frequently fail when applied without attention to local socio- cultural contexts (Shukla et al. 2023 ). In Dubai and the wider UAE, this challenge is compounded by the top-down nature of smart governance initiatives, which have been implemented with limited systematic assessment of resident perceptions related to risk, preparedness, and institutional capacity. (Al Muraqab, 2021 ). Emerging technologies add further complexity. Artificial intelligence (AI) is a core element of Dubai’s Smart City vision, with applications across transport, policing, energy, and emergency management (Dubai Government, 2017). Yet global evidence shows that technological adoption in resilience is fragile without public confidence in oversight, privacy protections, and transparency (Schintler and McNeely, 2022 ); (Yas et al. 2025 ). Understanding these sociotechnical trust factors is critical if AI is to move from a technical promise to a legitimate enabler of resilience. Based on these considerations, this study develops and applies a context-specific survey instrument designed to examine public perceptions related to infrastructure resilience in a rapidly expanding arid city. It focuses on four perception-based dimen- sions, risk awareness, preparedness, institutional confidence, and trust in artificial intelligence, which are treated as socio-technical signals influencing the feasibility, legitimacy, and public uptake of resilience policies. The study provides one of the first empirical examinations of AI trust within Dubai’s resilience context and identifies demographic and generational variations in perceived preparedness, institutional con- fidence, and technology acceptance. In doing so, it highlights the need to bridge the gap between visible, short-term hazards such as floods or cyber-attacks and slower- onset risks, including groundwater depletion and climate change. More broadly, the study contributes to resilience research across the Global South by offering an adapt- able framework suited to cities that share Dubai’s demographic diversity, ecological constraints, and governance complexity. This study responds to these gaps by examining public perceptions of resilience in Dubai, focusing on hazard awareness, preparedness, institutional confidence, and trust in AI-enabled systems. Using a structured questionnaire distributed to 121 respondents across diverse demographic and professional groups, it develops composite indices of preparedness, institutional confidence, public awareness, and AI trust. Results are interpreted in relation to Dubai’s ongoing resilience initiatives, including climate adap- tation projects, flood management, and smart city technologies. Three overarching research questions guide the study: W hat are the prevailing public perceptions of risks, hazards, and infrastructure resilience in Dubai? What is the level of preparedness do individuals feel to respond to potential disruptions, and what confidence do they place in authori- ties? To what extent are emerging technologies, particularly AI-driven solutions, trusted and viewed as enablers of resilience in Dubai? This paper proceeds as follows. Section 2 reviews the related literature, situating this study within debates on infrastructure resilience, social vulnerability, governance, and AI adoption. Building on these insights, Section 3 describes the survey method- ology and explains the construction of key indices used for analysis. Section 4 then presents the empirical results, highlighting patterns in risk perception, preparedness, institutional confidence, and trust in AI-enabled systems. These findings inform a set of perception-informed strategic pathways toward urban resilience, which are further examined in the Discussion section. Section 5 shows how they play out in Dubai’s social and governance context, making the ideas practical rather than abstract. Section 6 concludes by summarizing key insights and outlining directions for future research. 2 Related Work Urban resilience research has evolved along four major strands: (1) frameworks that assess infrastructure robustness in rapidly growing cities, (2) studies linking resilience to social vulnerability and governance, (3) research on risk perception and public awareness, and (4) emerging work on AI and smart technologies as resilience enablers. Together, these strands show that while technical systems are advancing, there are persistent gaps in how public perceptions, institutional trust, and technological accep- tance are integrated into resilience strategies. This study places itself at the intersection of these strands, with a particular focus on empirical evidence from Dubai. 2.1 Urban Resilience Frameworks in Rapidly Growing Cities Resilience assessment frameworks have expanded considerably in recent decades, yet their effectiveness in fast-growing cities remains contested (Sharifi, 2020 ). Studies on urban expansion reveal that Western frameworks often fail to capture the realities of the Global South, where rapid growth, informality, and climatic extremes create unique vulnerabilities (Parnell, 2016 , Shukla et al. 2023 ). For example, (Farrell, 2017 ) critiques uniform assessment models for neglecting demographic transitions, while (Nassar et al. 2014 ) show that Dubai’s hyper-arid growth patterns differ markedly from European and North American urban forms. Frameworks built around remote sensing and GIS have improved spatial monitoring (Uhl and Leyk, 2022 ), but they remain limited in integrating governance and social dimensions (Sharifi and Yamagata, 2016 ). While resilience frameworks have been conceptually extended to non-Western contexts, their applicability to hyper-arid and demographically diverse cities like Dubai remains under-examined, leaving questions about how they capture local governance and demographic complexity. 2.2 Linking Infrastructure Resilience to Social Vulnerability A growing body of research highlights that technical resilience cannot be separated from social vulnerability and governance. (Garschagen and Sandholz, 2017 ) propose integrative models linking infrastructure management with minimum supply standards and social vulnerability. (Kapucu et al. 2023 ) extend this by framing infrastructure resilience through a network governance perspective, emphasizing coordination across multiple actors. (Ferrari, 2020 ) argues for reflexive governance, where adaptive and redundant institutional capacity is as critical as physical redundancy. These studies suggest that in contexts like Dubai, where expatriate-majority demographics compli- cate social cohesion, resilience must explicitly incorporate trust, communication, and inclusivity alongside infrastructure robustness. Yet, there is little empirical evidence on how trust, inclusivity, and communication actually shape resilience in Dubai, where more than 80% of residents are expatriates. This study addresses that gap directly. 2.3 Risk Perceptions and Public Awareness Literature on risk perception shows that citizens often prioritize risks they have directly experienced and discount less tangible threats (Slovic, 2016 ). (Wilson et al. 2019 ) highlight the multidimensional nature of perceived risk, while (Alves et al. 2020 ) demonstrates how coping capacity remains low despite high awareness of multiple haz- ards. Recent studies confirm that risk perceptions vary significantly across geographic regions and social groups. For example, smallholder farmers in Mali exhibited risk attitudes that differed by gender, worldview, and region (Cullen et al. 2018 , while public concern in Italy and Sweden shifted away from long-term threats toward the more immediate and visible COVID-19 pandemic, illustrating the influence of hazard salience and national context on risk perception (Di Baldassarre et al. 2021 ). Studies confirm event-driven perceptions, but little is known about how these dynamics unfold in hyper-arid and demographically diverse cities such as Dubai, where hazard percep- tions are mediated by both local events and global mobility. This gap underscores the need for empirical evidence from the Gulf region. Despite global insights into event- driven risk perception, there is virtually no evidence on how these dynamics operate in Gulf cities. This study fills that gap by empirically analyzing hazard salience in Dubai’s diverse population. 2.4 Preparedness and Institutional Confidence Preparedness has long been identified as a critical determinant of urban resilience (Kim et al. 2025 ), yet the relationship between risk awareness and actual prepared- ness remains inconsistent (Wachinger et al. 2013 ). Multiple studies reveal significant nuances in this relationship. (Cisternas et al. 2024 ) found that while risk perception positively influences intention to prepare, awareness and preparedness are distinct concepts. (Scolobig et al. 2012 ) directly challenged the assumption that risk aware- ness leads to preparedness, revealing considerable discrepancies between perceived risk and actual protective actions. (Ning et al. 2021 ) further demonstrated that risk per- ception had the weakest effect (0.045) on emergency preparedness behaviors, with attitudes and self-efficacy being more influential. Similarly, (C. MacPherson-Krutsky et al. 2023 ) found information-seeking behavior to be the strongest influence on pre- paredness, suggesting that active engagement matters more than passive awareness. In Dubai, institutional capacity is globally recognized (World Bank, 2020 ), but the literature has not examined how this translates into public confidence, creating a gap that this study directly addresses. This highlights the need for empirical studies that link public preparedness and institutional trust, particularly in contexts where state capacity is high but public perceptions may diverge. 2.5 AI and Smart Technologies as Enablers of Resilience Emerging technologies, particularly AI, are increasingly positioned as resilience enablers (UNDRR, 2022). (Mohebbi et al. 2020 ) and (Yang et al. 2022 ) show how cyber-physical-social systems integrate technical and organizational processes, improv- ing disaster response and prediction. However, Recent work reveals that even when AI enables advanced cyber-physical-social integration in infrastructure resilience, its uptake is often constrained because users and institutions must trust how algorithms operate, understand the decision logic, and feel confident the systems are secure from cyber threats (Ghaffarian et al. 2023 ; Visave, 2025 ).For Dubai, where AI underpins the Smart City strategy and digital twin initiatives (Dubai Government, 2017), the literature suggests that public acceptance will hinge on addressing privacy concerns and ensuring inclusive governance of technological systems (Jobin et al. 2019 ). Despite Dubai’s strong AI-driven smart city agenda, there is limited evidence on how citizens and professionals evaluate such technologies as tools of resilience, creating an impor- tant research gap this study addresses. This raises a key question: can AI strengthen resilience if the public remains skeptical of its role? While AI adoption in urban resilience has been explored globally, its societal acceptance within Gulf smart-city contexts, including Dubai, is still insufficiently understood, especially regarding oversight, inclusivity, and privacy. 2.6 Synthesis and Research Gap Across these strands of literature, two key themes emerge. First, resilience frameworks must move beyond static, technical indicators to account for social vulnerability, gov- ernance, and trust. Second, while Dubai has been studied extensively as an example of rapid urban growth, little is known about how its residents perceive resilience, pre- paredness, and emerging technologies. Existing frameworks (e.g., Uhl and Leyk, 2022 ; Garschagen and Sand- holz, 2017) provide conceptual tools but lack empirical ground- ing in Dubai’s unique context of demographic diversity, ecological stress, and smart infrastructure ambitions. This study builds on that prior scholarship but advances the field by empirically analyzing how residents of Dubai perceive hazards, preparedness, institutional confidence, and AI-driven resilience solutions dimensions that remain underexplored in the literature on fast-growing Gulf cities. Taken together, these strands reveal that resilience in Dubai must be assessed not only through physical infrastructure but also through social perceptions, institutional trust, and technology acceptance. 3 Methodology This study adopts a quantitative exploratory design as the first phase of a broader mixed-methods research agenda. An exploratory design is particularly appropriate because there is currently no empirical baseline on how residents in Dubai perceive risks, preparedness, institutional confidence, and AI-enabled resilience. Establishing this baseline is a necessary step before moving to expert-driven refinement. The pri- mary objective is to capture public and stakeholder perceptions of resilience in Dubai, which will subsequently inform qualitative expert consultations (e.g., Delphi) in later phases. This phased design ensures that future expert discussions are anchored in the lived realities of residents rather than abstract technical assumptions. Figure 1 illustrates the methodological framework. The survey instrument, developed through a literature-informed process, comprised 65 items organized into five thematic modules: demographics, risk awareness, pre- paredness, institutional confidence, and AI trust. Items were adapted from validated scales in risk perception (Wachinger et al. 2013 ), disaster preparedness (Cisternas et al. 2024 ), and technology acceptance (Jobin et al. 2019 ), ensuring construct validity. To enhance contextual appropriateness, the questionnaire was piloted with experts in planning and governance, leading to refinements in clarity and cultural sensitivity. Data collection was conducted online via SurveyMonkey, yielding 121 valid responses. The sample included Emiratis (71.7%) and expatriates (28.3%), reflecting Dubai’s demographic complexity. Recruitment employed snowball sampling through academic, professional, and community networks. While the sample does not statisti- cally mirror Dubai’s expatriate-majority population, its distribution provides distinct advantages. Emirati voices are central to resilience governance, since nationals dom- inate leadership roles in government, security, and infrastructure. Their perspectives therefore provide critical insight into the institutional dimension of resilience, while expatriate responses highlight inclusivity gaps and shed light on how well non-nationals are integrated into resilience systems. Data analysis proceeded in four stages: Descriptive statistics to profile demographic and risk perceptions. Index construction for Preparedness, Institutional Confidence, Public Awareness, and AI Trust. Reliability Inferential testing (t-tests, cross-tabs) to assess differences across nationality and demographic groups. Correlation analysis to explore associations between preparedness, trust, and perceptions of AI. Beginning with a public perception survey, this study adds a complementary perspective, ensuring that the subsequent Delphi phase is informed not only by institutional expertise but also by the lived experiences of residents 3.1 Research Design The structured survey instrument comprised 65 items covering demographics, risk awareness, preparedness, institutional confidence, and AI trust. Items were adapted from validated scales in disaster preparedness and technology acceptance. This design is aligned with the literature on resilience measurement that emphasizes the integra- tion of technical, social, and institutional perspectives (Sharifi, 2020 ; Garschagen and Sand- holz, 2017). The survey instrument was specifically developed to capture per- ceptions of risk, preparedness, institutional trust, and attitudes toward AI, reflecting gaps previously identified in studies of Global South cities (Meerow and Stults, 2016 ; Shukla et al. 2023 ). The stepwise structure of the methodology, including question- naire design, piloting, data collection, and analysis, is illustrated in Fig. 1 , which summarizes the overall methodological framework of the study. 3.2 Instrument Development The Infrastructure Resilience Questionnaire comprised 65 items structured into five thematic modules: mographic and professional profile (age, nationality, education, occupation, experience, sector). Risk awareness and hazard perception, including exposure to natural (e.g., flooding, heatwaves) and man-made risks (e.g., cyber-attacks). Preparedness and response capacity, focusing on individual and organizational readiness measures. Institutional confidence, evaluating trust in government transparency, response capacity, and inclusivity. AI and smart technologies, addressing acceptance of digital innovations and concerns over cybersecurity and transparency. Items were formulated as multiple-choice and Likert-scale responses, drawing from validated instruments in disaster risk perception (Wachinger et al. 2013 ; Cisternas et al. 2024 ) and technology acceptance (Jobin et al. 2019 ). To ensure contextual relevance and cultural appropriateness, the questionnaire was piloted with experts in urban planning and infrastructure governance. The full survey questionnaire, including all items and response scales, is provided in the (Appendix 10). 3.3 Sampling and Data Collection The survey was distributed online via SurveyMonkey between [March 2025 to June 2025] and received 121 valid responses. Participants included both Emirati nationals and expatriates, reflecting Dubai’s demographic composition in which non-nationals account for over 80% of the population (GLMM, 2018). Respondents represented diverse sectors, including government, private industry, security, education, and media. Recruitment employed a snowball sampling approach through professional networks, academic channels, and community organizations, consistent with exploratory urban resilience studies in complex metropolitan contexts (Anguelovski et al. 2016 ). This over-representation of Emiratis is explicitly acknowledged. While not sta- tistically representative of Dubai’s wider population, Emirati voices are central to resilience governance, since nationals hold most decision-making and leadership roles in government, security, and infrastructure. Their perspectives therefore provide crit- ical insights into the institutional dimension of resilience, while expatriate responses capture complementary perspectives on inclusivity and trust. 3.4 Data Analysis Survey data were analyzed using SPSS v.28 and Microsoft Excel (SPSS 2021). The analysis followed four stages: Descriptive statistics to profile the sample and establish baseline perceptions of hazards. Index construction for Preparedness, Institutional Confidence, Public Awareness, and AI Trust, with reliability tested using Cronbach’s alpha. Inferential analysis (t-tests, cross-tabs) to identify demographic differences. Cross-tabulations and correlations were conducted to explore relationships among preparedness, institutional trust, and AI perceptions. This mixed analytical strategy allowed both descriptive and inferential insights into public perceptions of resilience in Dubai. 3.5 Ethical Considerations Participation in the study was voluntary and anonymous. Respondents were informed of the study purpose prior to participation, and no personal identifiers were collected. Ethical approval was obtained. 3.6 Limitations Two limitations are acknowledged. First, the sample size is modest and weighted toward Emiratis, which constrains representativeness given that non-nationals account for over 80% of Dubai’s population. However, this distribution provides valuable insights into resilience for two reasons. Emirati respondents are central to the institu- tional dimension of resilience, since nationals dominate decision-making and leadership roles in government, security, and infrastructure. Their perspectives therefore illu- minate how resilience is shaped at the governance level. Expatriate responses, by contrast, highlight inclusivity gaps and shed light on whether non-nationals who form the majority of residents are effectively integrated into preparedness and resilience sys- tems. Taken together, this distribution allows for a dual lens on resilience: institutional leadership and community inclusivity. Second, the cross-sectional design captures perceptions at a single point in time, which may evolve as Dubai’s resilience policies and technologies progress. These lim- itations will be addressed in Phase 2 through Delphi consultations with a panel of experts from government, academia, and civil society. The Delphi phase will (i) val- idate and refine the survey indices, (ii) mitigate demographic skew by integrating expert perspectives across sectors, and (iii) establish consensus on priority indicators for assessing resilience in Dubai. This phased design ensures that initial exploratory findings are both grounded in public perceptions and systematically validated by expert knowledge. 4 Results The survey yielded 121 valid responses. This section presents the results in five parts: demographics, risk awareness, preparedness, institutional confidence, and trust in AI and smart technologies. 4.1 Demographic Profile Respondents represented a young, highly educated, and predominantly employed segment of Dubai’s society. More than 60% were under 34 years, underscoring the youth-dominated character of the sample (Table 1 ). Emiratis made up 71.7% of partic- ipants, with expatriates accounting for just over a quarter, a distribution that reflects the survey’s professional and academic recruitment channels rather than Dubai’s wider expatriate-majority population. Nearly half of all respondents held a Bachelor’s degree, and two-thirds were employed across government, private, and semi-governmental sectors. This demographic profile suggests that the findings reflect the perspectives of Dubai’s engaged professional classes, who are closely connected to public institutions and policy discourses, but may under-represent the views of lower-income expatriate communities. Table 1 Sample demographics (valid responses = 121) Characteristic n Valid % Age group (n = 92) Under 18 4 4.3 18–24 32 34.8 25–34 26 28.3 35–44 20 21.7 45–54 7 7.6 55–64 1 1.1 65+ 2 2.2 Nationality (n = 92) Emirati 66 71.7 Non-Emirati 26 28.3 Highest education (n = 89) High School 25 28.1 Bachelor’s 42 47.2 Master’s 12 13.5 PhD 6 6.7 Other 4 4.5 Employment status (n = 90) Employed 57 66.3 Student 17 20.2 Unemployed 6 6.7 Self-employed 4 4.5 Retired 2 2.2 4.2 Risk Awareness and Hazard Perception Respondents identified a clear set of hazards as most critical for Dubai’s infrastruc- ture resilience. Flooding, heatwaves, and transport disruptions clearly dominate public concern (Fig. 2 ). Their prominence reflects both recent crises, such as the disrup- tive 2024 floods, and the city’s structural vulnerabilities as a hyper-arid, high-density metropolis. By contrast, hazards with lower historical occurrence, such as earthquakes or terrorism, were ranked far lower. This confirms that risk awareness in Dubai is strongly event-driven: visible and recent hazards dominate attention, while chronic risks such as groundwater depletion or sea-level rise remain under-recognized. Information channels also revealed important patterns.Three-quarters of respon- dents relied on news outlets, followed by government announcements and social media, while only a small minority cited community networks (Table 2 ). This reliance on top-down communication strengthens official reach but limits grassroots engagement, potentially reducing community-driven preparedness. These results confirm that hazard salience in Dubai is strongly shaped by imme- diacy and visibility. Resilience planning must therefore bridge the gap between short-term public perceptions and longer-term, less tangible risks if it is to build legitimacy and inclusivity. Table 2 Primary information sources for risks and emergencies Source Responses (n) % of cases News outlets 50 75.8 Government announcements 46 69.7 Social media 41 62.1 Community networks 15 22.7 Other 3 4.5 Note : Multiple responses allowed. Percent of cases is calculated by SPSS for the dichotomy group at value 1. 4.3 Preparedness Although hazard awareness was widespread, concrete readiness was limited. Fewer than one in five respondents felt fully prepared, while the majority described them- selves as only “somewhat prepared” and a substantial share admitted they would struggle or were unsure how to respond. Emiratis reported marginally higher prepared- ness (19.7% fully prepared) than expatriates (15.4%), but expatriates were almost twice as likely to feel unprepared or unsure (46.1% vs. 39.4%) (Fig. 3 ). The Preparedness Index achieved the highest internal reliability, Cronbach’s α = 0.827, confirming readiness behaviors formed a coherent construct across groups (Table 3). Although Emiratis had slightly higher mean scores than expatriates, the differ- ence was not statistically significant (Table 4). Thus, nationality explains only part of the preparedness gap, and other factors such as individual awareness, access to resources, or prior experience likely influence readiness. These results reinforce that while awareness of hazards is widespread, full readiness remains limited, particularly among expatriates and less-established groups. The results confirm an institutional awareness-preparedness gap. While respon- dents recognize hazards, this awareness has not translated into consistent readiness. The sharper gap among expatriates suggests that Dubai’s preparedness initiatives are not yet inclusive, leaving a large segment of residents outside the city’s resilience systems. Table 3 Internal consistency of indices (Cronbach’s alpha) Index Items grouped Cronbach’s α Preparedness 6 items (training, readiness, response) 0.827 Institutional Confidence 3 items (trust, transparency, inclusivity) 0.499 AI Trust / Acceptance 5 items (AI alerts, oversight, privacy, data sharing, training) 0.689 Table 4 : Preparedness Index scores by nationality (Independent samples t-test) Group N Mean (SD) t-test (p) Emirati 66 3.79 (0.81) t = 0.94, p = 0.35 Non-Emirati 26 3.65 (0.76) – Note : Preparedness Index (6 items). 4.4 Institutional Confidence Confidence in local authorities’ capacity to manage disasters revealed a mixed pic- ture. On average, respondents expressed moderate to high confidence, ranging between “somewhat confident” and “very confident.” However, the Institutional Confidence Index had a Cronbach’s αonly 0.499 (Table 4), indicating weak internal consistency and fragmented perceptions across indicators of trust, transparency, and inclusivity. This suggests that “institutional confidence” is not a cohesive or consistently perceived con- struct in Dubai’s context, reflecting divided views across different institutions rather than a unified sense of trust. Emiratis reported slightly higher confidence (mean = 3.42) than expatriates (mean = 3.21), though the difference was not statistically significant (Table 5 ). Yet expa- triates were nearly twice as likely to report low trust (23.1% vs. 12.1%) (Table 6 ). These results suggest a dual reality: residents broadly trust the technical capability of institutions, but doubts persist around inclusivity and representation. Expatri- ates in particular feel less confident, raising concerns that resilience measures are not perceived as equitable across Dubai’s diverse population. Table 5 Institutional Confidence scores by nationality (Independent samples t- test) Group N Mean (SD) t-test (p) Emirati 66 3.42 (0.71) t = 1.56, p = 0.12 Non-Emirati 26 3.21 (0.68) – Note : Institutional Confidence Index (3 items). (Values from SPSS independent samples t-test output: Emiratis slightly higher mean, but the difference was not statistically significant at p < 0.05.) Table 6 Confidence in government emergency response by nationality (% within group) Nationality Very confident Somewhat confident Not confident Emirati (n = 66) 42.4% 45.5% 12.1% Non-Emirati (n = 26) 34.6% 42.3% 23.1% 4.5 Trust in AI and Smart Technologies Perceptions of AI as an enabler of resilience were cautiously optimistic but quali- fied by concerns over oversight, privacy, and inclusivity. Nearly half of respondents (45.9%) believed AI could strengthen resilience “to some extent,” while 14.8% believed it could do so “significantly.” Trust in AI-powered alerts was highly conditional: most respondents stated they would only rely on such systems if accompanied by human oversight. Data-sharing willingness was split almost evenly between positive, nega- tive, and uncertain responses, underscoring hesitation to provide personal data for resilience purposes. Interest in AI-enabled training simulations was modest, with only about one-third expressing enthusiasm. The AI Trust/Acceptance Index had a Cronbach’s αof 0.689, reflecting moderate but acceptable internal reliability (Table 3 ). Emiratis reported significantly higher trust (mean = 3.61) than expatriates (mean = 3.28), with the difference reaching statistical significance (t = 2.05, p = 0.04) (Table 7 ). This suggests that national belonging influences levels of trust in AI applications, with expatriates more skepti- cal about both institutional oversight and data privacy. A generational divide also emerged, younger cohorts expressed higher trust in AI systems, while older respondents were more cautious. Results demonstrate conditional optimism toward AI. Respondents see its potential to enhance resilience, but only under frameworks that ensure human accountability, privacy safeguards, and inclusivity. For policy, this means resilience systems cannot rely on youth adoption alone. While younger residents may adapt quickly to digital platforms and AI-enabled tools, strategies must also address the skepticism of older populations to ensure that trust and preparedness are inclusive across age groups. Table 7 AI Trust/Acceptance scores by nationality (Independent samples t-test) Group N Mean (SD) t-test (p) Emirati 66 3.61 (0.74) t = 2.05, p = 0.04* Non-Emirati 26 3.28 (0.69) – Note : AI Trust Index (5 items). Emiratis report significantly higher trust than expatriates ( p < 0.05). An asterisk (*) denotes p < 0.05. 4.6 Age-Group Comparison Age emerged as a significant differentiator in attitudes toward AI, but not institutional confidence. Respondents aged 18–34 reported significantly higher trust in AI (mean = 3.72) compared to those aged 35+ (mean = 3.39; t = 2.18, p = 0.03) (Table 8 ). This indicates that younger respondents, who are more digitally embedded, are more open to AI as a resilience tool. By contrast, institutional confidence showed no significant difference between younger (3.28) and older (3.43) groups (t = 1.04, p = 0.30) (Table 9 ). This suggests that evaluations of government capacity are shared across generations. Trust in AI-powered alerts was more nuanced. Younger respondents were more likely to accept alerts with oversight (55%) or full trust (28%), while older respondents were less likely to express unconditional trust (17%) and more likely to reject AI alerts altogether (37%) (Fig. 4 ). The generational divide in AI trust illustrates how resilience strategies must be tailored to different demographic groups. Younger respondents, more digitally embed- ded, were open to AI integration, while older respondents expressed skepticism and demanded oversight. By contrast, institutional confidence did not vary significantly by age, suggesting that governance perceptions are shared across generations. These findings underscore the importance of differentiated communication strategies and blended human–AI governance approaches to ensure inclusivity. 4.6.1 AI Trust/Acceptance by age group Younger respondents (18–34 years) reported significantly higher trust in AI applica- tions for resilience (mean = 3.72, SD = 0.68) compared with respondents aged 35 and above (mean = 3.39, SD = 0.71). This difference was statistically significant (t = 2.18, p = 0.03) (Table 8 ). These results suggest that younger populations, who are more digitally embedded, are more open to adopting AI as part of resilience strategies. Table 8 AI Trust/Acceptance scores by age group (Independent samples t-test) Age group N Mean (SD) t-test (p) 18–34 years 58 3.72 (0.68) t = 2.18, p = 0.03* 35 + years 30 3.39 (0.71) – Note : AI Trust Index (5 items). Younger respondents show significantly higher trust in AI for resilience applications ( p < 0.05). An asterisk (*) denotes p < 0.05. 4.6.2 Institutional Confidence by age group In contrast, institutional confidence did not differ significantly by age. Younger respon- dents averaged 3.28, while older respondents averaged 3.43 (t = 1.04, p = 0.30) (Table 9 ). This indicates that evaluations of government preparedness and response are shared relatively evenly across generations, suggesting that perceptions of institutional capacity are less influenced by age than trust in technology. Table 9 Institutional Confidence scores by age group (Independent samples t- test) Age group N Mean (SD) t-test (p) 18–34 years 58 3.28 (0.66) t = 1.04, p = 0.30 35 + years 30 3.43 (0.72) – Note : Institutional Confidence Index (3 items). No significant difference between age groups. 4.6.3 Trust in AI-powered alerts by age group Trust in AI alerts showed a sharper generational divide. Younger respondents were far more likely to express conditional or full trust: 28.3% reported full trust and 55% conditional trust with oversight, leaving only 16.7% unwilling to trust AI alerts. Among older respondents, by contrast, just 16.7% expressed full trust, 46.7% trusted with oversight, and 36.6% reported no trust in AI alerts (Fig. 4 ). This generational divergence underscores that AI adoption strategies must be age- sensitive. Younger groups may accept digital tools more readily, while older groups require stronger assurances of oversight, accountability, and reliability before placing trust in AI-driven systems. Together, the findings point to several opportunities to strengthen infrastructure resilience in Dubai that differ from those typically assumed in the Gulf context. The dominance of visible hazards such as flooding suggests that public awareness campaigns must also address less tangible but equally consequential risks, including groundwater depletion and extreme heat. The strong link between institutional confi- dence and preparedness, and the substantially lower preparedness among expatriates, indicates that resilience planning must account for demographic divides within the city’s population structure. In addition, the conditional nature of trust in AI-enabled systems highlights that adoption alone is insufficient without transparency and safe- guards that reflect public expectations. Collectively, these insights extend existing resilience research by demonstrating how sociotechnical trust, demographic compo- sition, and hazard visibility interact to shape infrastructure resilience in hyper-arid, fast-growing cities. Overall, the results reveal clear contrasts in how different groups perceive risks, prepare for disruptions, and place confidence in both institutions and AI-enabled sys- tems. Visible and high-impact hazards dominate public awareness, preparedness varies sharply between Emirati and expatriate residents, and trust in AI remains conditional on transparency and oversight. These patterns collectively highlight underlying social and technological vulnerabilities that shape Dubai’s resilience landscape. The follow- ing discussion examines these findings in greater depth, situating them within existing scholarship and exploring their broader implications for urban resilience in hyper-arid, fast-growing cities. 5 Discussion This discussion interprets the study’s findings through a perception-based lens. The policy implications presented below are derived from observed patterns in public risk awareness, preparedness, institutional confidence, and trust in AI-enabled systems, rather than from a direct evaluation of infrastructure performance, institutional capac- ity, or system-level resilience outcomes. The discussion therefore, focuses on how these perception patterns condition the legitimacy, feasibility, and public uptake of resilience strategies in Dubai and similar hyper-arid, fast-growing urban contexts. This section interprets the findings by moving beyond the three research questions to identify cross-cutting themes that shape the implementation and social reception of resilience strategies in Dubai. Five thematic insights emerge: preparedness gaps, institutional trust dynamics, AI trust fragility, inclusivity in resilience, and the pol- icy practice disconnect. These themes illustrate how social perceptions, demographic divides, and governance challenges intersect with Dubai’s ambitious resilience agenda. To strengthen coherence with the study objectives, this discussion is explicitly structured around the three research questions. Research Question 1 examines per- ceived urban hazards. Research Question 2 focuses on preparedness and institutional confidence. Research Question 3 addresses public trust in AI-enabled resilience tools. The policy implications presented below are derived from empirically observed per- ception patterns. They are therefore framed as governance, communication, and engagement interventions, rather than as evaluations of technical system performance. 5.1 Preparedness Gaps This subsection addresses Research Question 2 by interpreting reported preparedness levels and confidence in institutional response. Survey results indicate that fewer than one-fifth of respondents consider themselves fully prepared. Expatriate residents were significantly more likely to report uncertainty or lack of preparedness. Despite widespread awareness of hazards, fewer than one in five respondents felt fully prepared. Most described themselves as only “somewhat prepared,” and nearly half of expatriates reported being unprepared or unsure. This reveals a clear awareness- preparedness gap, echoing global studies showing that hazard recognition does not automatically translate into readiness. The Preparedness Index’s strong reliability (Cronbach’s α = 0.827) confirms readiness is a coherent construct, but its low mean scores highlight the need for systematic training and drills. The persistence of this gap, even alongside world-class resilience investments such as the Aquifer Storage and Recovery (ASR) system (DEWA, 2024), suggests that technical redundancy alone does not ensure household-level preparedness. These findings suggest that voluntary preparedness initiatives may be insufficient in reaching all population groups. Pol- icy efforts should therefore prioritize institutionalized preparedness mechanisms, such as workplace-based drills, building-level protocols, and standardized public training formats. 5.2 Institutional Trust Dynamics Respondents expressed moderate confidence in Dubai’s institutions, but this trust was fragmented across transparency, inclusivity, and representation. These findings con- tribute to Research Question 2 by clarifying how preparedness outcomes are shaped by differentiated perceptions of institutional confidence. Emiratis were slightly more con- fident than expatriates, yet nearly a quarter of expatriates reported low trust compared to just over one in ten Emiratis. This dual reality suggests that while technical capacity is respected, institutional legitimacy is perceived as uneven across demographic groups. The low internal consistency of the institutional confidence index further indicates that respondents do not perceive institutional trust as a single, unified construct. Instead, confidence appears differentiated across response capacity, transparency, and inclusiv- ity. The contrast is reflected in projects such as the Waste-to-Energy facility at Warsan, which is technically impressive (2023a), yet not widely perceived by residents as central to resilience. Policy responses should therefore address institutional confidence through multiple, targeted channels. These include clearer communication of institutional response roles, improved transparency in decision-making processes, and mechanisms for meaningful public participation. Treating institutional trust as a multidimensional outcome may enhance the effectiveness of urban resilience governance. 5.3 AI Trust Fragility This subsection addresses Research Question 3 by examining public attitudes toward AI-enabled resilience tools. Survey results indicate conditional support for AI appli- cations, with acceptance dependent on human oversight, data protection, and system transparency. Nearly half of respondents said they would trust AI alerts only under human oversight, and willingness to share personal data was evenly divided. Emi- ratis and younger cohorts reported higher trust, while expatriates and older groups were more skeptical. These patterns mirror Dubai’s Smart Governance agenda, where AI systems are increasingly deployed in policing and urban management (Dubai Police, 2023 b). While such innovations demonstrate efficiency, public trust remains conditional on oversight, transparency, and privacy safeguards. Conditional optimism toward AI, therefore, presents both opportunity and risk. On one hand, the Smart City agenda positions Dubai as a global hub for technological innovation. On the other hand, skepticism about oversight and inclusivity poses reputational risks if communi- ties perceive AI systems as opaque or exclusionary, weakening confidence in Dubai’s broader innovation narrative. These findings support a governance-oriented approach to AI deployment. Human-in-the-loop decision structures, explainable alert systems, and explicit data governance protocols are likely to be essential for sustaining public trust. 5.4 Inclusivity in Resilience Findings relevant to Research Questions 1 through 3 reveal systematic differences across demographic groups. Emirati nationals and younger respondents reported higher preparedness and institutional confidence, while older and expatriate residents consistently reported lower levels. Across preparedness, institutional trust, and AI acceptance, a recurring theme was demographic divides. Emiratis and younger cohorts consistently reported higher preparedness, stronger confidence, and greater openness to AI, while expatriates and older residents expressed lower trust and readiness. These disparities show that per- ceived preparedness, confidence, and technology acceptance are not distributed evenly but mediated by nationality, age, and social integration. Large-scale initiatives such as the Mohammed bin Rashid Al Maktoum Solar Park (DEWA, 2024) illustrate Dubai’s long-term sustainability ambitions, yet without inclusive communication and train- ing, such flagship investments will not bridge these divides. For resilience strategies to be effective, inclusivity must be treated as a core principle rather than an optional add-on. These disparities highlight the need for inclusive resilience strategies. Multilin- gual communication, employer-mediated preparedness programs, and community-level engagement mechanisms may be particularly effective in reaching underrepresented groups. 5.5 Policy-Practice Disconnect The persistence of flooding as the highest-ranked perceived hazard suggests a gap between infrastructure investment narratives and public awareness. This does not imply deficiencies in technical performance. Rather, it indicates limited visibility of resilience measures at the household and community levels. Dubai has invested heavily in mega-projects such as the Deep Tunnel Stormwater System (2021), large-scale solar parks, the Aquifer Storage and Recovery system, and digital twins, yet these measures are not always matched by public awareness, preparedness, or trust. Flooding was identified as the top hazard despite the large-scale tunnel project, and residents con- tinue to reference recent disruptions, such as the 2024 floods, as shaping perceptions of preparedness through lived experience. Similarly, initiatives such as waste-to-energy or renewable energy infrastructure remain largely invisible to most residents. Improving the public legibility of resilience investments may therefore be as important as their physical implementation. Localized communication and community-facing explana- tions of infrastructure functions could strengthen perceived safety and preparedness. 5.6 Perception-Informed Pathways for Urban Resilience The findings indicate that public hazard perception, preparedness, institutional con- fidence, and trust in digital systems interact to shape urban resilience outcomes. Effective policy therefore depends not only on the scale of resilience investments, but also on how these measures are interpreted and trusted by diverse population groups. Persistent emphasis on flooding and recent disruptions suggests that long- term infrastructure and governance initiatives are not always translated into everyday understandings of risk reduction. Strengthening the visibility and interpret-ability of resilience interventions at household and community levels may help align public perception with planning objectives. Preparedness and institutional confidence emerge as mutually reinforcing, par- ticularly when supported through routine institutional settings such as workplaces and community organizations. Reliance on voluntary engagement alone is unlikely to achieve consistent coverage across demographic groups, underscoring the need for structured and inclusive preparedness mechanisms. Trust in AI-enabled resilience tools further highlights the importance of governance, as public acceptance remains conditional on transparency, accountability, and human oversight. Integrating these principles into digital resilience initiatives, while addressing demographic disparities through targeted communication and engagement, may support both incremental adaptation and more trans-formative resilience pathways. Based on the thematic findings of the study, Fig. 5 integrates the three research questions into a directional, perception-informed analytical framework in which each component influences the next through explicit arrow-based pathways. Public percep- tions of hazards and disruptions (RQ1) shape how risks are framed and prioritized, influencing the consideration of baseline, adaptive, or trans-formative resilience path- ways. Levels of perceived preparedness and institutional confidence (RQ2) condition the feasibility and social acceptance of response strategies, mediating how these strategies interact with exposure and vulnerability considerations within the broader resilience context. Attitudes toward AI-enabled systems and technology acceptance (RQ3) operate as both an enabling factor and a moderating influence, shaping how data-driven and automated tools are received and trusted by the public. The framework visualizes directional relationships through which the three research questions collectively inform successive stages of resilience decision-making, yielding a dynamic rather than linear understanding of resilience. Its staged and feedback- oriented structure highlights how social perceptions, institutional confidence, and technological readiness interact over time to shape the resilience pathways available to fast-growing cities facing intensifying climatic and demographic pressures. Rather than framing resilience as the outcome of isolated indicators, the framework emphasizes the interdependence of behavioural, institutional, and technological dimensions. This integrated perspective provides a conceptual foundation for the concluding section, which synthesizes the study’s empirical contributions and examines their implications for advancing infrastructure resilience in rapidly evolving urban contexts. 6 Conclusion This study addresses its central objective of examining how residents in a hyper-arid Gulf city perceive urban resilience. It focuses on hazards, preparedness, institutional confidence, and AI-enabled systems. The findings identify three interrelated vulnera- bilities, visible hazard bias, uneven preparedness, and conditional trust in AI, which shape how resilience challenges are understood and acted upon at the societal level. These perception-based dynamics are not fully captured by assessments that prioritize technical capacity alone. A key contribution of the study lies in the development of an integrated, perception- informed analytical framework grounded in the stated research objectives. The framework links public perceptions to staged resilience decision processes, including scenario framing and policy learning pathways. Rather than evaluating infrastructure performance, it demonstrates how behavioural, institutional, and technological dimen- sions interact over time to influence the social reception and governance of resilience strategies. Public perceptions and AI trust are therefore treated as core analytical variables, not contextual background factors. From a policy perspective, the findings indicate the need to broaden risk awareness beyond highly visible hazards. They also highlight the importance of strengthening preparedness across demographic groups. Institutional confidence depends on trans- parency and inclusive engagement. AI-enabled resilience systems require accountable governance to sustain public trust. Future research may extend the study’s objectives through expert validation and comparative application across other Gulf and Global South cities. Taken together, the study advances a perceptio-centred understanding of urban resilience, aligned with its original aims and relevant to rapidly evolving urban contexts. Declarations Author Contribution H.A. designed the study, developed the survey instrument, collected the data, and led the analysis. Also drafted the main manuscript text.M.M. contributed to conceptual development, contextual analysis, and critical revisions of the manuscript.Y.R. provided methodological guidance, supervised the analytical framework, and contributed to revising the text.T.B. supported the statistical analysis, advised on data interpretation, and reviewed the manuscript for technical accuracy.All authors reviewed and approved the final manuscript. 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14:38:47","extension":"xml","order_by":53,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167800,"visible":true,"origin":"","legend":"","description":"","filename":"342c6b8c109d490ea0a3774ee6a096821structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/351cbdcf07d68a56091efc86.xml"},{"id":99813313,"identity":"a04f7471-d261-4db0-98c4-0fa88b677856","added_by":"auto","created_at":"2026-01-08 14:38:49","extension":"html","order_by":54,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":189875,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/a4d6243a1f60d175d33b4547.html"},{"id":99813401,"identity":"ec634060-3b39-4b00-8473-fdcdfb7f931e","added_by":"auto","created_at":"2026-01-08 14:39:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43552,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological plan and process adopted in this study, illustrating key stages from survey design and piloting to data collection, analysis, and validation.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/5ab6c960a10fe4fb9df0aff8.jpg"},{"id":99813543,"identity":"7b2c20f8-c0e3-4632-a9a1-b2e53be5b1f4","added_by":"auto","created_at":"2026-01-08 14:39:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20521,"visible":true,"origin":"","legend":"\u003cp\u003eTop Five Hazards as Most Critical for Dubai’s Infrastructure\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/cd8340b445b2e4d44b3ad606.jpg"},{"id":99812954,"identity":"0ea39e4f-9739-4c77-93c1-58b14b035691","added_by":"auto","created_at":"2026-01-08 14:38:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19902,"visible":true,"origin":"","legend":"\u003cp\u003eSelf-reported preparedness for emergencies by nationality\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/da70e57502a37cede7f5bdde.jpg"},{"id":99813284,"identity":"ae050925-34fa-4224-a20d-ef38696dd816","added_by":"auto","created_at":"2026-01-08 14:38:48","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14985,"visible":true,"origin":"","legend":"\u003cp\u003eTrust in AI-powered alerts by age group (% within group)\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/ed30778932613110ed2e7fdd.jpg"},{"id":100356229,"identity":"596a67dc-3c35-41d7-b683-272c9479f0e1","added_by":"auto","created_at":"2026-01-16 06:58:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35828,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual perception-informed framework linking public hazard perception, preparedness, institutional confidence, and trust in digital systems to future resilience planning processes.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/a132c5cba1d837c07afa2a3b.jpg"},{"id":100376719,"identity":"4f644f42-5b6f-4a29-99dc-b63cb0a4e598","added_by":"auto","created_at":"2026-01-16 08:45:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1433316,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/faf65e06-e7fa-4fd2-aa0c-238d33b4d4ad.pdf"},{"id":99813490,"identity":"52d7fd5e-ce42-4dfd-819a-fc74fcc8d916","added_by":"auto","created_at":"2026-01-08 14:39:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46996,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8490604/v1/2eab16baf3f750c48ed0b3df.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Infrastructure Resilience in Hyper-Arid fast-growing Cities: Public Perceptions, Institutional Confidence, and AI Trust in Dubai","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUrban resilience has become a defining concern of the twenty-first century. As cities expand at unprecedented rates, they face mounting risks from climate change, environ- mental degradation, and complex interdependencies across infrastructure systems. The United Nations Office for Disaster Risk Reduction (UNDRR) and the Sendai Frame- work for Disaster Risk Reduction (2015\u0026ndash;2030) emphasise that enhancing resilience requires both technical capacity and social engagement, including risk awareness, pub- lic trust, and inclusive governance (UNDRR, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; UNDRR, 2022). The Sustainable Development Goals (SDG 11) further reinforce the need for cities to be \u0026ldquo;inclusive, safe, resilient, and sustainable,\u0026rdquo; highlighting the importance of linking infrastructure performance with public preparedness and institutional accountability (UNHabitat, 2023).\u003c/p\u003e \u003cp\u003eArid and hyper-arid urban regions are especially exposed to compound risks (IPCC \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; UNHabitat, 2023). In Dubai, natural aquifer recharge is minimal, leaving only 3% of groundwater fresh and forcing reliance on desalination and aquifer storage (Gonzalez et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Demand pressures are intensifying, with electricity consump- tion reaching 56.5 TWh in 2023, a 6.3% annual increase driven by cooling needs (Saberi-Derakhtenjani et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while peak summer temperatures may exceed 50\u0026deg;C, compounding stress on infrastructure (Park et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rapid urban expansion has further strained ecosystems and governance capacity (UNDESA, 2022; (Awad and Jung, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Environmental stresses, including rising air pollution (Akasha et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), add to systemic vulnerabilities. Acute shocks also intersect with these chronic pres- sures: major floods in 2024 disrupted transport and caused insured losses of USD 2.9\u0026ndash;3.4\u0026nbsp;billion (Francis et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; (Allam et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These overlapping challenges underline that resilience in Dubai cannot be achieved through infrastructure capacity alone, but is also conditioned by how residents perceive risks, prepare for disruptions, and trust institutional and technological responses.\u003c/p\u003e \u003cp\u003eDubai provides a critical case for examining these dynamics. The city\u0026rsquo;s population has grown more than tenfold since the 1970s, with urban expansion rates exceeding 10% annually in the early 2000s (Nassar et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Today, it is home to over 3.6\u0026nbsp;million people, projected to rise to 5.8\u0026nbsp;million by 2040 (Government of Dubai, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). More than 80% of residents are expatriates (GLMM, 2018), making them central to the city\u0026rsquo;s resilience. Yet they remain underrepresented in formal governance and policy- making. Their perceptions matter not only because they constitute the overwhelming majority of residents, but also because gaps in preparedness, awareness, or institutional confidence can undermine the effective implementation of resilience strategies.\u003c/p\u003e \u003cp\u003eCapturing these perceptions is therefore essential. Globally, resilience research has shown that frameworks often remain conceptual and struggle to connect theory with practice (Meerow and Stults, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Anguelovski et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the Global South, Western-centric models frequently fail when applied without attention to local socio- cultural contexts (Shukla et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Dubai and the wider UAE, this challenge is compounded by the top-down nature of smart governance initiatives, which have been implemented with limited systematic assessment of resident perceptions related to risk, preparedness, and institutional capacity. (Al Muraqab, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmerging technologies add further complexity. Artificial intelligence (AI) is a core element of Dubai\u0026rsquo;s Smart City vision, with applications across transport, policing, energy, and emergency management (Dubai Government, 2017). Yet global evidence shows that technological adoption in resilience is fragile without public confidence in oversight, privacy protections, and transparency (Schintler and McNeely, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); (Yas et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Understanding these sociotechnical trust factors is critical if AI is to move from a technical promise to a legitimate enabler of resilience.\u003c/p\u003e \u003cp\u003eBased on these considerations, this study develops and applies a context-specific survey instrument designed to examine public perceptions related to infrastructure resilience in a rapidly expanding arid city. It focuses on four perception-based dimen- sions, risk awareness, preparedness, institutional confidence, and trust in artificial intelligence, which are treated as socio-technical signals influencing the feasibility, legitimacy, and public uptake of resilience policies. The study provides one of the first empirical examinations of AI trust within Dubai\u0026rsquo;s resilience context and identifies demographic and generational variations in perceived preparedness, institutional con- fidence, and technology acceptance. In doing so, it highlights the need to bridge the gap between visible, short-term hazards such as floods or cyber-attacks and slower- onset risks, including groundwater depletion and climate change. More broadly, the study contributes to resilience research across the Global South by offering an adapt- able framework suited to cities that share Dubai\u0026rsquo;s demographic diversity, ecological constraints, and governance complexity.\u003c/p\u003e \u003cp\u003eThis study responds to these gaps by examining public perceptions of resilience in Dubai, focusing on hazard awareness, preparedness, institutional confidence, and trust in AI-enabled systems. Using a structured questionnaire distributed to 121 respondents across diverse demographic and professional groups, it develops composite indices of preparedness, institutional confidence, public awareness, and AI trust. Results are interpreted in relation to Dubai\u0026rsquo;s ongoing resilience initiatives, including climate adap- tation projects, flood management, and smart city technologies. Three overarching research questions guide the study:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eW\u003cb\u003ehat are the prevailing public perceptions of risks, hazards, and infrastructure resilience in Dubai?\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eWhat is the level of preparedness do individuals feel to respond to potential disruptions, and what confidence do they place in authori- ties?\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTo what extent are emerging technologies, particularly AI-driven solutions, trusted and viewed as enablers of resilience in Dubai?\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis paper proceeds as follows. Section 2 reviews the related literature, situating this study within debates on infrastructure resilience, social vulnerability, governance, and AI adoption. Building on these insights, Section 3 describes the survey method- ology and explains the construction of key indices used for analysis. Section 4 then presents the empirical results, highlighting patterns in risk perception, preparedness, institutional confidence, and trust in AI-enabled systems. These findings inform a set\u003c/p\u003e \u003cp\u003eof perception-informed strategic pathways toward urban resilience, which are further examined in the Discussion section. Section 5 shows how they play out in Dubai\u0026rsquo;s social and governance context, making the ideas practical rather than abstract. Section 6 concludes by summarizing key insights and outlining directions for future research.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2 Related Work","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUrban resilience research has evolved along four major strands: (1) frameworks that assess infrastructure robustness in rapidly growing cities, (2) studies linking resilience to social vulnerability and governance, (3) research on risk perception and public awareness, and (4) emerging work on AI and smart technologies as resilience enablers. Together, these strands show that while technical systems are advancing, there are persistent gaps in how public perceptions, institutional trust, and technological accep- tance are integrated into resilience strategies. This study places itself at the intersection of these strands, with a particular focus on empirical evidence from Dubai.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Urban Resilience Frameworks in Rapidly Growing Cities\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eResilience assessment frameworks have expanded considerably in recent decades, yet their effectiveness in fast-growing cities remains contested (Sharifi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies on urban expansion reveal that Western frameworks often fail to capture the realities of the Global South, where rapid growth, informality, and climatic extremes create unique vulnerabilities (Parnell, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Shukla et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For example, (Farrell, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) critiques uniform assessment models for neglecting demographic transitions, while (Nassar et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) show that Dubai\u0026rsquo;s hyper-arid growth patterns differ markedly from European and North American urban forms. Frameworks built around remote sensing and GIS have improved spatial monitoring (Uhl and Leyk, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but they remain limited in integrating governance and social dimensions (Sharifi and Yamagata, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). While resilience frameworks have been conceptually extended to non-Western contexts, their applicability to hyper-arid and demographically diverse cities like Dubai remains under-examined, leaving questions about how they capture local governance and demographic complexity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Linking Infrastructure Resilience to Social Vulnerability\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA growing body of research highlights that technical resilience cannot be separated from social vulnerability and governance. (Garschagen and Sandholz, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) propose integrative models linking infrastructure management with minimum supply standards and social vulnerability. (Kapucu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) extend this by framing infrastructure resilience through a network governance perspective, emphasizing coordination across multiple actors. (Ferrari, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argues for reflexive governance, where adaptive and redundant institutional capacity is as critical as physical redundancy. These studies suggest that in contexts like Dubai, where expatriate-majority demographics compli- cate social cohesion, resilience must explicitly incorporate trust, communication, and inclusivity alongside infrastructure robustness. Yet, there is little empirical evidence\u003c/p\u003e \u003cp\u003eon how trust, inclusivity, and communication actually shape resilience in Dubai, where more than 80% of residents are expatriates. This study addresses that gap directly.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Risk Perceptions and Public Awareness\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eLiterature on risk perception shows that citizens often prioritize risks they have directly experienced and discount less tangible threats (Slovic, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). (Wilson et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) highlight the multidimensional nature of perceived risk, while (Alves et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrates how coping capacity remains low despite high awareness of multiple haz- ards. Recent studies confirm that risk perceptions vary significantly across geographic regions and social groups. For example, smallholder farmers in Mali exhibited risk attitudes that differed by gender, worldview, and region (Cullen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, while public concern in Italy and Sweden shifted away from long-term threats toward the more immediate and visible COVID-19 pandemic, illustrating the influence of hazard salience and national context on risk perception (Di Baldassarre et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies confirm event-driven perceptions, but little is known about how these dynamics unfold in hyper-arid and demographically diverse cities such as Dubai, where hazard percep- tions are mediated by both local events and global mobility. This gap underscores the need for empirical evidence from the Gulf region. Despite global insights into event- driven risk perception, there is virtually no evidence on how these dynamics operate in Gulf cities. This study fills that gap by empirically analyzing hazard salience in Dubai\u0026rsquo;s diverse population.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Preparedness and Institutional Confidence\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePreparedness has long been identified as a critical determinant of urban resilience (Kim et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), yet the relationship between risk awareness and actual prepared- ness remains inconsistent (Wachinger et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Multiple studies reveal significant nuances in this relationship. (Cisternas et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that while risk perception positively influences intention to prepare, awareness and preparedness are distinct concepts. (Scolobig et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) directly challenged the assumption that risk aware- ness leads to preparedness, revealing considerable discrepancies between perceived risk and actual protective actions. (Ning et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further demonstrated that risk per- ception had the weakest effect (0.045) on emergency preparedness behaviors, with attitudes and self-efficacy being more influential. Similarly, (C. MacPherson-Krutsky et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found information-seeking behavior to be the strongest influence on pre- paredness, suggesting that active engagement matters more than passive awareness. In Dubai, institutional capacity is globally recognized (World Bank, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but the literature has not examined how this translates into public confidence, creating a gap that this study directly addresses. This highlights the need for empirical studies that link public preparedness and institutional trust, particularly in contexts where state capacity is high but public perceptions may diverge.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 AI and Smart Technologies as Enablers of Resilience\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEmerging technologies, particularly AI, are increasingly positioned as resilience enablers (UNDRR, 2022). (Mohebbi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and (Yang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) show how cyber-physical-social systems integrate technical and organizational processes, improv- ing disaster response and prediction. However, Recent work reveals that even when AI enables advanced cyber-physical-social integration in infrastructure resilience, its uptake is often constrained because users and institutions must trust how algorithms operate, understand the decision logic, and feel confident the systems are secure from cyber threats (Ghaffarian et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Visave, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).For Dubai, where AI underpins the Smart City strategy and digital twin initiatives (Dubai Government, 2017), the literature suggests that public acceptance will hinge on addressing privacy concerns and ensuring inclusive governance of technological systems (Jobin et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite Dubai\u0026rsquo;s strong AI-driven smart city agenda, there is limited evidence on how citizens and professionals evaluate such technologies as tools of resilience, creating an impor- tant research gap this study addresses. This raises a key question: can AI strengthen resilience if the public remains skeptical of its role?\u003c/p\u003e \u003cp\u003eWhile AI adoption in urban resilience has been explored globally, its societal acceptance within Gulf smart-city contexts, including Dubai, is still insufficiently understood, especially regarding oversight, inclusivity, and privacy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Synthesis and Research Gap\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAcross these strands of literature, two key themes emerge. First, resilience frameworks must move beyond static, technical indicators to account for social vulnerability, gov- ernance, and trust. Second, while Dubai has been studied extensively as an example of rapid urban growth, little is known about how its residents perceive resilience, pre- paredness, and emerging technologies. Existing frameworks (e.g., Uhl and Leyk, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garschagen and Sand- holz, 2017) provide conceptual tools but lack empirical ground- ing in Dubai\u0026rsquo;s unique context of demographic diversity, ecological stress, and smart infrastructure ambitions. This study builds on that prior scholarship but advances the field by empirically analyzing how residents of Dubai perceive hazards, preparedness, institutional confidence, and AI-driven resilience solutions dimensions that remain underexplored in the literature on fast-growing Gulf cities. Taken together, these strands reveal that resilience in Dubai must be assessed not only through physical infrastructure but also through social perceptions, institutional trust, and technology acceptance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study adopts a quantitative exploratory design as the first phase of a broader mixed-methods research agenda. An exploratory design is particularly appropriate because there is currently no empirical baseline on how residents in Dubai perceive risks, preparedness, institutional confidence, and AI-enabled resilience. Establishing this baseline is a necessary step before moving to expert-driven refinement. The pri- mary objective is to capture public and stakeholder perceptions of resilience in Dubai,\u003c/p\u003e \u003cp\u003ewhich will subsequently inform qualitative expert consultations (e.g., Delphi) in later phases. This phased design ensures that future expert discussions are anchored in the lived realities of residents rather than abstract technical assumptions. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the methodological framework.\u003c/p\u003e \u003cp\u003eThe survey instrument, developed through a literature-informed process, comprised 65 items organized into five thematic modules: demographics, risk awareness, pre- paredness, institutional confidence, and AI trust. Items were adapted from validated scales in risk perception (Wachinger et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), disaster preparedness (Cisternas et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and technology acceptance (Jobin et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), ensuring construct validity. To enhance contextual appropriateness, the questionnaire was piloted with experts in planning and governance, leading to refinements in clarity and cultural sensitivity.\u003c/p\u003e \u003cp\u003eData collection was conducted online via SurveyMonkey, yielding 121 valid responses. The sample included Emiratis (71.7%) and expatriates (28.3%), reflecting Dubai\u0026rsquo;s demographic complexity. Recruitment employed snowball sampling through academic, professional, and community networks. While the sample does not statisti- cally mirror Dubai\u0026rsquo;s expatriate-majority population, its distribution provides distinct advantages. Emirati voices are central to resilience governance, since nationals dom- inate leadership roles in government, security, and infrastructure. Their perspectives therefore provide critical insight into the institutional dimension of resilience, while expatriate responses highlight inclusivity gaps and shed light on how well non-nationals are integrated into resilience systems.\u003c/p\u003e \u003cp\u003eData analysis proceeded in four stages:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDescriptive statistics to profile demographic and risk perceptions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndex construction for Preparedness, Institutional Confidence, Public Awareness, and AI Trust. Reliability\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInferential testing (t-tests, cross-tabs) to assess differences across nationality and demographic groups.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCorrelation analysis to explore associations between preparedness, trust, and perceptions of AI.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBeginning with a public perception survey, this study adds a complementary perspective, ensuring that the subsequent Delphi phase is informed not only by institutional expertise but also by the lived experiences of residents\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe structured survey instrument comprised 65 items covering demographics, risk awareness, preparedness, institutional confidence, and AI trust. Items were adapted from validated scales in disaster preparedness and technology acceptance. This design is aligned with the literature on resilience measurement that emphasizes the integra- tion of technical, social, and institutional perspectives (Sharifi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garschagen and Sand- holz, 2017). The survey instrument was specifically developed to capture per- ceptions of risk, preparedness, institutional trust, and attitudes toward AI, reflecting gaps previously identified in studies of Global South cities (Meerow and Stults, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e;\u003c/p\u003e \u003cp\u003eShukla et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The stepwise structure of the methodology, including question- naire design, piloting, data collection, and analysis, is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which summarizes the overall methodological framework of the study.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Instrument Development\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe Infrastructure Resilience Questionnaire comprised 65 items structured into five thematic modules:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003emographic and professional profile (age, nationality, education, occupation, experience, sector).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRisk awareness and hazard perception, including exposure to natural (e.g., flooding, heatwaves) and man-made risks (e.g., cyber-attacks).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePreparedness and response capacity, focusing on individual and organizational readiness measures.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInstitutional confidence, evaluating trust in government transparency, response capacity, and inclusivity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAI and smart technologies, addressing acceptance of digital innovations and concerns over cybersecurity and transparency.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eItems were formulated as multiple-choice and Likert-scale responses, drawing from validated instruments in disaster risk perception (Wachinger et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cisternas et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and technology acceptance (Jobin et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To ensure contextual relevance and cultural appropriateness, the questionnaire was piloted with experts in urban planning and infrastructure governance. The full survey questionnaire, including all items and response scales, is provided in the (Appendix 10).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sampling and Data Collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe survey was distributed online via SurveyMonkey between [March 2025 to June 2025] and received 121 valid responses. Participants included both Emirati nationals and expatriates, reflecting Dubai\u0026rsquo;s demographic composition in which non-nationals account for over 80% of the population (GLMM, 2018). Respondents represented diverse sectors, including government, private industry, security, education, and media. Recruitment employed a snowball sampling approach through professional networks, academic channels, and community organizations, consistent with exploratory urban resilience studies in complex metropolitan contexts (Anguelovski et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis over-representation of Emiratis is explicitly acknowledged. While not sta- tistically representative of Dubai\u0026rsquo;s wider population, Emirati voices are central to resilience governance, since nationals hold most decision-making and leadership roles in government, security, and infrastructure. Their perspectives therefore provide crit- ical insights into the institutional dimension of resilience, while expatriate responses capture complementary perspectives on inclusivity and trust.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Data Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSurvey data were analyzed using SPSS v.28 and Microsoft Excel (SPSS 2021). The analysis followed four stages:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDescriptive statistics to profile the sample and establish baseline perceptions of hazards.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndex construction for Preparedness, Institutional Confidence, Public Awareness, and AI Trust, with reliability tested using Cronbach\u0026rsquo;s alpha.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eInferential analysis (t-tests, cross-tabs) to identify demographic differences.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCross-tabulations and correlations were conducted to explore relationships among preparedness, institutional trust, and AI perceptions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis mixed analytical strategy allowed both descriptive and inferential insights into public perceptions of resilience in Dubai.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Ethical Considerations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eParticipation in the study was voluntary and anonymous. Respondents were informed of the study purpose prior to participation, and no personal identifiers were collected. Ethical approval was obtained.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Limitations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTwo limitations are acknowledged. First, the sample size is modest and weighted toward Emiratis, which constrains representativeness given that non-nationals account for over 80% of Dubai\u0026rsquo;s population. However, this distribution provides valuable insights into resilience for two reasons. Emirati respondents are central to the institu- tional dimension of resilience, since nationals dominate decision-making and leadership roles in government, security, and infrastructure. Their perspectives therefore illu- minate how resilience is shaped at the governance level. Expatriate responses, by contrast, highlight inclusivity gaps and shed light on whether non-nationals who form the majority of residents are effectively integrated into preparedness and resilience sys- tems. Taken together, this distribution allows for a dual lens on resilience: institutional leadership and community inclusivity.\u003c/p\u003e \u003cp\u003eSecond, the cross-sectional design captures perceptions at a single point in time, which may evolve as Dubai\u0026rsquo;s resilience policies and technologies progress. These lim- itations will be addressed in Phase 2 through Delphi consultations with a panel of experts from government, academia, and civil society. The Delphi phase will (i) val- idate and refine the survey indices, (ii) mitigate demographic skew by integrating expert perspectives across sectors, and (iii) establish consensus on priority indicators for assessing resilience in Dubai. This phased design ensures that initial exploratory findings are both grounded in public perceptions and systematically validated by expert knowledge.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe survey yielded 121 valid responses. This section presents the results in five parts: demographics, risk awareness, preparedness, institutional confidence, and trust in AI and smart technologies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Demographic Profile\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRespondents represented a young, highly educated, and predominantly employed segment of Dubai\u0026rsquo;s society. More than 60% were under 34 years, underscoring the youth-dominated character of the sample (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Emiratis made up 71.7% of partic- ipants, with expatriates accounting for just over a quarter, a distribution that reflects the survey\u0026rsquo;s professional and academic recruitment channels rather than Dubai\u0026rsquo;s wider expatriate-majority population. Nearly half of all respondents held a Bachelor\u0026rsquo;s degree, and two-thirds were employed across government, private, and semi-governmental sectors.\u003c/p\u003e \u003cp\u003eThis demographic profile suggests that the findings reflect the perspectives of Dubai\u0026rsquo;s engaged professional classes, who are closely connected to public institutions and policy discourses, but may under-represent the views of lower-income expatriate communities.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample demographics (valid responses\u0026thinsp;=\u0026thinsp;121)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValid %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge group\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;92) Under 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNationality\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;92)\u003c/p\u003e \u003cp\u003eEmirati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Emirati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHighest education\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;89) High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEmployment status\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;90) Employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Risk Awareness and Hazard Perception\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRespondents identified a clear set of hazards as most critical for Dubai\u0026rsquo;s infrastruc- ture resilience. Flooding, heatwaves, and transport disruptions clearly dominate public concern (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Their prominence reflects both recent crises, such as the disrup- tive 2024 floods, and the city\u0026rsquo;s structural vulnerabilities as a hyper-arid, high-density metropolis. By contrast, hazards with lower historical occurrence, such as earthquakes or terrorism, were ranked far lower. This confirms that risk awareness in Dubai is strongly event-driven: visible and recent hazards dominate attention, while chronic risks such as groundwater depletion or sea-level rise remain under-recognized.\u003c/p\u003e \u003cp\u003eInformation channels also revealed important patterns.Three-quarters of respon- dents relied on news outlets, followed by government announcements and social media, while only a small minority cited community networks (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This reliance on\u003c/p\u003e \u003cp\u003etop-down communication strengthens official reach but limits grassroots engagement, potentially reducing community-driven preparedness.\u003c/p\u003e \u003cp\u003eThese results confirm that hazard salience in Dubai is strongly shaped by imme- diacy and visibility. Resilience planning must therefore bridge the gap between short-term public perceptions and longer-term, less tangible risks if it is to build legitimacy and inclusivity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimary information sources for risks and emergencies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponses (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% of cases\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNews outlets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment announcements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial media\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity networks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e: Multiple responses allowed. Percent of cases is calculated by SPSS for the dichotomy group at value 1.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Preparedness\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAlthough hazard awareness was widespread, concrete readiness was limited. Fewer than one in five respondents felt fully prepared, while the majority described them- selves as only \u0026ldquo;somewhat prepared\u0026rdquo; and a substantial share admitted they would struggle or were unsure how to respond. Emiratis reported marginally higher prepared- ness (19.7% fully prepared) than expatriates (15.4%), but expatriates were almost twice as likely to feel unprepared or unsure (46.1% vs. 39.4%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Preparedness Index achieved the highest internal reliability, Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.827, confirming readiness behaviors formed a coherent construct across groups (Table\u003c/p\u003e \u003cp\u003e3). Although Emiratis had slightly higher mean scores than expatriates, the differ- ence was not statistically significant (Table\u0026nbsp;4). Thus, nationality explains only part of the preparedness gap, and other factors such as individual awareness, access to resources, or prior experience likely influence readiness. These results reinforce that while awareness of hazards is widespread, full readiness remains limited, particularly among expatriates and less-established groups.\u003c/p\u003e \u003cp\u003eThe results confirm an institutional awareness-preparedness gap. While respon- dents recognize hazards, this awareness has not translated into consistent readiness. The sharper gap among expatriates suggests that Dubai\u0026rsquo;s preparedness initiatives are not yet inclusive, leaving a large segment of residents outside the city\u0026rsquo;s resilience systems.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInternal consistency of indices (Cronbach\u0026rsquo;s alpha)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItems grouped\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePreparedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 items (training, readiness, response)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInstitutional Confidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 items (trust, transparency, inclusivity)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAI Trust / Acceptance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 items (AI alerts, oversight, privacy, data sharing, training)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTable\u0026nbsp;4\u003c/b\u003e: Preparedness Index scores by nationality (Independent samples t-test)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGroup\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003et-test (p)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmirati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.79 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;0.94, p\u0026thinsp;=\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Emirati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.65 (0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: Preparedness Index (6 items).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Institutional Confidence\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eConfidence in local authorities\u0026rsquo; capacity to manage disasters revealed a mixed pic- ture. On average, respondents expressed moderate to high confidence, ranging between \u0026ldquo;somewhat confident\u0026rdquo; and \u0026ldquo;very confident.\u0026rdquo; However, the Institutional Confidence Index had a Cronbach\u0026rsquo;s \u003cem\u003eαonly\u003c/em\u003e0.499 (Table\u0026nbsp;4), indicating weak internal consistency and fragmented perceptions across indicators of trust, transparency, and inclusivity. This suggests that \u0026ldquo;institutional confidence\u0026rdquo; is not a cohesive or consistently perceived con- struct in Dubai\u0026rsquo;s context, reflecting divided views across different institutions rather than a unified sense of trust.\u003c/p\u003e \u003cp\u003eEmiratis reported slightly higher confidence (mean\u0026thinsp;=\u0026thinsp;3.42) than expatriates (mean\u003c/p\u003e \u003cp\u003e=\u0026thinsp;3.21), though the difference was not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Yet expa- triates were nearly twice as likely to report low trust (23.1% vs. 12.1%) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese results suggest a dual reality: residents broadly trust the technical capability of institutions, but doubts persist around inclusivity and representation. Expatri- ates in particular feel less confident, raising concerns that resilience measures are not perceived as equitable across Dubai\u0026rsquo;s diverse population.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstitutional Confidence scores by nationality (Independent samples t- test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-test (p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmirati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.42 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;1.56, p\u0026thinsp;=\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Emirati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.21 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: Institutional Confidence Index (3 items).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e(Values from SPSS independent samples t-test output: Emiratis slightly higher mean, but the difference was not statistically significant at \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05.)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfidence in government emergency response by nationality (% within group)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery confident\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSomewhat confident\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot confident\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmirati (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Emirati (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Trust in AI and Smart Technologies\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePerceptions of AI as an enabler of resilience were cautiously optimistic but quali- fied by concerns over oversight, privacy, and inclusivity. Nearly half of respondents (45.9%) believed AI could strengthen resilience \u0026ldquo;to some extent,\u0026rdquo; while 14.8% believed it could do so \u0026ldquo;significantly.\u0026rdquo; Trust in AI-powered alerts was highly conditional: most respondents stated they would only rely on such systems if accompanied by human oversight. Data-sharing willingness was split almost evenly between positive, nega- tive, and uncertain responses, underscoring hesitation to provide personal data for resilience purposes. Interest in AI-enabled training simulations was modest, with only about one-third expressing enthusiasm.\u003c/p\u003e \u003cp\u003eThe AI Trust/Acceptance Index had a Cronbach\u0026rsquo;s \u003cem\u003eαof\u003c/em\u003e 0.689, reflecting moderate but acceptable internal reliability (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Emiratis reported significantly higher trust (mean\u0026thinsp;=\u0026thinsp;3.61) than expatriates (mean\u0026thinsp;=\u0026thinsp;3.28), with the difference reaching statistical significance (t\u0026thinsp;=\u0026thinsp;2.05, p\u0026thinsp;=\u0026thinsp;0.04) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This suggests that national belonging influences levels of trust in AI applications, with expatriates more skepti- cal about both institutional oversight and data privacy. A generational divide also emerged, younger cohorts expressed higher trust in AI systems, while older respondents were more cautious.\u003c/p\u003e \u003cp\u003eResults demonstrate conditional optimism toward AI. Respondents see its potential to enhance resilience, but only under frameworks that ensure human accountability, privacy safeguards, and inclusivity. For policy, this means resilience systems cannot rely on youth adoption alone. While younger residents may adapt quickly to digital platforms and AI-enabled tools, strategies must also address the skepticism of older populations to ensure that trust and preparedness are inclusive across age groups.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI Trust/Acceptance scores by nationality (Independent samples t-test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-test (p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmirati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.61 (0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.05, p\u0026thinsp;=\u0026thinsp;0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Emirati\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.28 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: AI Trust Index (5 items). Emiratis report significantly higher trust than expatriates (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). An asterisk (*) denotes \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Age-Group Comparison\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAge emerged as a significant differentiator in attitudes toward AI, but not institutional confidence. Respondents aged 18\u0026ndash;34 reported significantly higher trust in AI (mean\u0026thinsp;=\u0026thinsp;3.72) compared to those aged 35+ (mean\u0026thinsp;=\u0026thinsp;3.39; t\u0026thinsp;=\u0026thinsp;2.18, p\u0026thinsp;=\u0026thinsp;0.03) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This indicates that younger respondents, who are more digitally embedded, are more open to AI as a resilience tool.\u003c/p\u003e \u003cp\u003eBy contrast, institutional confidence showed no significant difference between younger (3.28) and older (3.43) groups (t\u0026thinsp;=\u0026thinsp;1.04, p\u0026thinsp;=\u0026thinsp;0.30) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This suggests that evaluations of government capacity are shared across generations.\u003c/p\u003e \u003cp\u003eTrust in AI-powered alerts was more nuanced. Younger respondents were more likely to accept alerts with oversight (55%) or full trust (28%), while older respondents were less likely to express unconditional trust (17%) and more likely to reject AI alerts altogether (37%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe generational divide in AI trust illustrates how resilience strategies must be tailored to different demographic groups. Younger respondents, more digitally embed- ded, were open to AI integration, while older respondents expressed skepticism and demanded oversight. By contrast, institutional confidence did not vary significantly by age, suggesting that governance perceptions are shared across generations. These findings underscore the importance of differentiated communication strategies and blended human\u0026ndash;AI governance approaches to ensure inclusivity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.6.1 AI Trust/Acceptance by age group\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eYounger respondents (18\u0026ndash;34 years) reported significantly higher trust in AI applica- tions for resilience (mean\u0026thinsp;=\u0026thinsp;3.72, SD\u0026thinsp;=\u0026thinsp;0.68) compared with respondents aged 35 and above (mean\u0026thinsp;=\u0026thinsp;3.39, SD\u0026thinsp;=\u0026thinsp;0.71). This difference was statistically significant (t\u0026thinsp;=\u0026thinsp;2.18, p\u0026thinsp;=\u0026thinsp;0.03) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These results suggest that younger populations, who are more digitally embedded, are more open to adopting AI as part of resilience strategies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAI Trust/Acceptance scores by age group (Independent samples t-test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-test (p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.72 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;2.18, p\u0026thinsp;=\u0026thinsp;0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.39 (0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: AI Trust Index (5 items). Younger respondents show significantly higher trust in AI for resilience applications (\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). An asterisk (*) denotes \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.6.2 Institutional Confidence by age group\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn contrast, institutional confidence did not differ significantly by age. Younger respon- dents averaged 3.28, while older respondents averaged 3.43 (t\u0026thinsp;=\u0026thinsp;1.04, p\u0026thinsp;=\u0026thinsp;0.30) (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This indicates that evaluations of government preparedness and response are shared relatively evenly across generations, suggesting that perceptions of institutional capacity are less influenced by age than trust in technology.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstitutional Confidence scores by age group (Independent samples t- test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-test (p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;34 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.28 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;1.04, p\u0026thinsp;=\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.43 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote\u003c/em\u003e: Institutional Confidence Index (3 items). No significant difference between age groups.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.6.3 Trust in AI-powered alerts by age group\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTrust in AI alerts showed a sharper generational divide. Younger respondents were far more likely to express conditional or full trust: 28.3% reported full trust and 55% conditional trust with oversight, leaving only 16.7% unwilling to trust AI alerts. Among older respondents, by contrast, just 16.7% expressed full trust, 46.7% trusted with oversight, and 36.6% reported no trust in AI alerts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis generational divergence underscores that AI adoption strategies must be age- sensitive. Younger groups may accept digital tools more readily, while older groups require stronger assurances of oversight, accountability, and reliability before placing trust in AI-driven systems.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTogether, the findings point to several opportunities to strengthen infrastructure resilience in Dubai that differ from those typically assumed in the Gulf context. The dominance of visible hazards such as flooding suggests that public awareness campaigns must also address less tangible but equally consequential risks, including groundwater depletion and extreme heat. The strong link between institutional confi- dence and preparedness, and the substantially lower preparedness among expatriates, indicates that resilience planning must account for demographic divides within the city\u0026rsquo;s population structure. In addition, the conditional nature of trust in AI-enabled systems highlights that adoption alone is insufficient without transparency and safe- guards that reflect public expectations. Collectively, these insights extend existing resilience research by demonstrating how sociotechnical trust, demographic compo- sition, and hazard visibility interact to shape infrastructure resilience in hyper-arid, fast-growing cities.\u003c/p\u003e \u003cp\u003eOverall, the results reveal clear contrasts in how different groups perceive risks, prepare for disruptions, and place confidence in both institutions and AI-enabled sys- tems. Visible and high-impact hazards dominate public awareness, preparedness varies sharply between Emirati and expatriate residents, and trust in AI remains conditional on transparency and oversight. These patterns collectively highlight underlying social and technological vulnerabilities that shape Dubai\u0026rsquo;s resilience landscape. The follow- ing discussion examines these findings in greater depth, situating them within existing scholarship and exploring their broader implications for urban resilience in hyper-arid, fast-growing cities.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis discussion interprets the study\u0026rsquo;s findings through a perception-based lens. The policy implications presented below are derived from observed patterns in public risk awareness, preparedness, institutional confidence, and trust in AI-enabled systems, rather than from a direct evaluation of infrastructure performance, institutional capac- ity, or system-level resilience outcomes. The discussion therefore, focuses on how these perception patterns condition the legitimacy, feasibility, and public uptake of resilience strategies in Dubai and similar hyper-arid, fast-growing urban contexts.\u003c/p\u003e \u003cp\u003eThis section interprets the findings by moving beyond the three research questions to identify cross-cutting themes that shape the implementation and social reception of resilience strategies in Dubai. Five thematic insights emerge: preparedness gaps, institutional trust dynamics, AI trust fragility, inclusivity in resilience, and the pol- icy practice disconnect. These themes illustrate how social perceptions, demographic divides, and governance challenges intersect with Dubai\u0026rsquo;s ambitious resilience agenda.\u003c/p\u003e \u003cp\u003eTo strengthen coherence with the study objectives, this discussion is explicitly structured around the three research questions. Research Question 1 examines per- ceived urban hazards. Research Question 2 focuses on preparedness and institutional confidence. Research Question 3 addresses public trust in AI-enabled resilience tools. The policy implications presented below are derived from empirically observed per- ception patterns. They are therefore framed as governance, communication, and engagement interventions, rather than as evaluations of technical system performance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Preparedness Gaps\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis subsection addresses Research Question 2 by interpreting reported preparedness levels and confidence in institutional response. Survey results indicate that fewer than one-fifth of respondents consider themselves fully prepared. Expatriate residents were significantly more likely to report uncertainty or lack of preparedness.\u003c/p\u003e \u003cp\u003eDespite widespread awareness of hazards, fewer than one in five respondents felt fully prepared. Most described themselves as only \u0026ldquo;somewhat prepared,\u0026rdquo; and nearly half of expatriates reported being unprepared or unsure. This reveals a clear awareness- preparedness gap, echoing global studies showing that hazard recognition does not automatically translate into readiness. The Preparedness Index\u0026rsquo;s strong reliability (Cronbach\u0026rsquo;s \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.827) confirms readiness is a coherent construct, but its low mean scores highlight the need for systematic training and drills. The persistence of this gap, even alongside world-class resilience investments such as the Aquifer Storage and Recovery (ASR) system (DEWA, 2024), suggests that technical redundancy alone does not ensure household-level preparedness. These findings suggest that voluntary preparedness initiatives may be insufficient in reaching all population groups. Pol- icy efforts should therefore prioritize institutionalized preparedness mechanisms, such as workplace-based drills, building-level protocols, and standardized public training formats.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Institutional Trust Dynamics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRespondents expressed moderate confidence in Dubai\u0026rsquo;s institutions, but this trust was fragmented across transparency, inclusivity, and representation. These findings con- tribute to Research Question 2 by clarifying how preparedness outcomes are shaped by differentiated perceptions of institutional confidence. Emiratis were slightly more con- fident than expatriates, yet nearly a quarter of expatriates reported low trust compared to just over one in ten Emiratis. This dual reality suggests that while technical capacity is respected, institutional legitimacy is perceived as uneven across demographic groups. The low internal consistency of the institutional confidence index further indicates that respondents do not perceive institutional trust as a single, unified construct. Instead, confidence appears differentiated across response capacity, transparency, and inclusiv- ity. The contrast is reflected in projects such as the Waste-to-Energy facility at Warsan, which is technically impressive (2023a), yet not widely perceived by residents as central to resilience. Policy responses should therefore address institutional confidence through multiple, targeted channels. These include clearer communication of institutional response roles, improved transparency in decision-making processes, and mechanisms for meaningful public participation. Treating institutional trust as a multidimensional outcome may enhance the effectiveness of urban resilience governance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.3 AI Trust Fragility\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis subsection addresses Research Question 3 by examining public attitudes toward AI-enabled resilience tools. Survey results indicate conditional support for AI appli- cations, with acceptance dependent on human oversight, data protection, and system transparency. Nearly half of respondents said they would trust AI alerts only under human oversight, and willingness to share personal data was evenly divided. Emi- ratis and younger cohorts reported higher trust, while expatriates and older groups were more skeptical. These patterns mirror Dubai\u0026rsquo;s Smart Governance agenda, where AI systems are increasingly deployed in policing and urban management (Dubai Police, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003eb). While such innovations demonstrate efficiency, public trust remains conditional on oversight, transparency, and privacy safeguards. Conditional optimism toward AI, therefore, presents both opportunity and risk. On one hand, the Smart City agenda positions Dubai as a global hub for technological innovation. On the other hand, skepticism about oversight and inclusivity poses reputational risks if communi- ties perceive AI systems as opaque or exclusionary, weakening confidence in Dubai\u0026rsquo;s broader innovation narrative. These findings support a governance-oriented approach to AI deployment. Human-in-the-loop decision structures, explainable alert systems, and explicit data governance protocols are likely to be essential for sustaining public trust.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Inclusivity in Resilience\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFindings relevant to Research Questions 1 through 3 reveal systematic differences across demographic groups. Emirati nationals and younger respondents reported higher preparedness and institutional confidence, while older and expatriate residents consistently reported lower levels.\u003c/p\u003e \u003cp\u003eAcross preparedness, institutional trust, and AI acceptance, a recurring theme was demographic divides. Emiratis and younger cohorts consistently reported higher preparedness, stronger confidence, and greater openness to AI, while expatriates and older residents expressed lower trust and readiness. These disparities show that per- ceived preparedness, confidence, and technology acceptance are not distributed evenly but mediated by nationality, age, and social integration. Large-scale initiatives such as the Mohammed bin Rashid Al Maktoum Solar Park (DEWA, 2024) illustrate Dubai\u0026rsquo;s long-term sustainability ambitions, yet without inclusive communication and train- ing, such flagship investments will not bridge these divides. For resilience strategies to be effective, inclusivity must be treated as a core principle rather than an optional add-on. These disparities highlight the need for inclusive resilience strategies. Multilin- gual communication, employer-mediated preparedness programs, and community-level engagement mechanisms may be particularly effective in reaching underrepresented groups.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Policy-Practice Disconnect\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe persistence of flooding as the highest-ranked perceived hazard suggests a gap between infrastructure investment narratives and public awareness. This does not imply deficiencies in technical performance. Rather, it indicates limited visibility of resilience measures at the household and community levels. Dubai has invested heavily in mega-projects such as the Deep Tunnel Stormwater System (2021), large-scale solar parks, the Aquifer Storage and Recovery system, and digital twins, yet these measures are not always matched by public awareness, preparedness, or trust. Flooding was identified as the top hazard despite the large-scale tunnel project, and residents con- tinue to reference recent disruptions, such as the 2024 floods, as shaping perceptions of preparedness through lived experience. Similarly, initiatives such as waste-to-energy or renewable energy infrastructure remain largely invisible to most residents. Improving the public legibility of resilience investments may therefore be as important as their physical implementation. Localized communication and community-facing explana- tions of infrastructure functions could strengthen perceived safety and preparedness.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Perception-Informed Pathways for Urban Resilience\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe findings indicate that public hazard perception, preparedness, institutional con- fidence, and trust in digital systems interact to shape urban resilience outcomes. Effective policy therefore depends not only on the scale of resilience investments, but also on how these measures are interpreted and trusted by diverse population groups. Persistent emphasis on flooding and recent disruptions suggests that long- term infrastructure and governance initiatives are not always translated into everyday understandings of risk reduction. Strengthening the visibility and interpret-ability of resilience interventions at household and community levels may help align public perception with planning objectives.\u003c/p\u003e \u003cp\u003ePreparedness and institutional confidence emerge as mutually reinforcing, par- ticularly when supported through routine institutional settings such as workplaces and community organizations. Reliance on voluntary engagement alone is unlikely to achieve consistent coverage across demographic groups, underscoring the need for structured and inclusive preparedness mechanisms. Trust in AI-enabled resilience tools further highlights the importance of governance, as public acceptance remains conditional on transparency, accountability, and human oversight. Integrating these principles into digital resilience initiatives, while addressing demographic disparities through targeted communication and engagement, may support both incremental adaptation and more trans-formative resilience pathways.\u003c/p\u003e \u003cp\u003eBased on the thematic findings of the study, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e integrates the three research questions into a directional, perception-informed analytical framework in which each component influences the next through explicit arrow-based pathways. Public percep- tions of hazards and disruptions (RQ1) shape how risks are framed and prioritized, influencing the consideration of baseline, adaptive, or trans-formative resilience path- ways. Levels of perceived preparedness and institutional confidence (RQ2) condition the feasibility and social acceptance of response strategies, mediating how these strategies interact with exposure and vulnerability considerations within the broader resilience context. Attitudes toward AI-enabled systems and technology acceptance (RQ3) operate as both an enabling factor and a moderating influence, shaping how data-driven and automated tools are received and trusted by the public.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe framework visualizes directional relationships through which the three research questions collectively inform successive stages of resilience decision-making, yielding a dynamic rather than linear understanding of resilience. Its staged and feedback- oriented structure highlights how social perceptions, institutional confidence, and technological readiness interact over time to shape the resilience pathways available to fast-growing cities facing intensifying climatic and demographic pressures. Rather than framing resilience as the outcome of isolated indicators, the framework emphasizes the interdependence of behavioural, institutional, and technological dimensions. This integrated perspective provides a conceptual foundation for the concluding section, which synthesizes the study\u0026rsquo;s empirical contributions and examines their implications for advancing infrastructure resilience in rapidly evolving urban contexts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study addresses its central objective of examining how residents in a hyper-arid Gulf city perceive urban resilience. It focuses on hazards, preparedness, institutional confidence, and AI-enabled systems. The findings identify three interrelated vulnera- bilities, visible hazard bias, uneven preparedness, and conditional trust in AI, which shape how resilience challenges are understood and acted upon at the societal level. These perception-based dynamics are not fully captured by assessments that prioritize technical capacity alone.\u003c/p\u003e \u003cp\u003eA key contribution of the study lies in the development of an integrated, perception- informed analytical framework grounded in the stated research objectives. The framework links public perceptions to staged resilience decision processes, including scenario framing and policy learning pathways. Rather than evaluating infrastructure performance, it demonstrates how behavioural, institutional, and technological dimen- sions interact over time to influence the social reception and governance of resilience strategies. Public perceptions and AI trust are therefore treated as core analytical variables, not contextual background factors.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, the findings indicate the need to broaden risk awareness beyond highly visible hazards. They also highlight the importance of strengthening preparedness across demographic groups. Institutional confidence depends on trans- parency and inclusive engagement. AI-enabled resilience systems require accountable governance to sustain public trust. Future research may extend the study\u0026rsquo;s objectives through expert validation and comparative application across other Gulf and Global South cities. Taken together, the study advances a perceptio-centred understanding of urban resilience, aligned with its original aims and relevant to rapidly evolving urban contexts.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.A. designed the study, developed the survey instrument, collected the data, and led the analysis. Also drafted the main manuscript text.M.M. contributed to conceptual development, contextual analysis, and critical revisions of the manuscript.Y.R. provided methodological guidance, supervised the analytical framework, and contributed to revising the text.T.B. supported the statistical analysis, advised on data interpretation, and reviewed the manuscript for technical accuracy.All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe survey data generated and analyzed during this study are not publicly available due to confidentiality and ethical restrictions, as participants were assured anonymity and no personal identifiers were collected. De-identified datasets may be made available from the corresponding author upon reasonable request and subject to institutional ethical approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlves, P.B.R., Cordao, M.J.d.S., Djordjevi\u0026acute;c, S., Javadi, A.A.: Place-based citizen sci- ence for assessing risk perception and coping capacity of households affected by multiple hazards. Sustainability 13(1), 302 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllam, Z., Cheshmehzangi, A., Khavarian-Garmsir, A.R.: Climate change and the cost of rapid urbanization: planning lessons from dubai\u0026rsquo;s flood. Discover Cities 1(1), 16 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkasha, H., Ghaffarpasand, O., Pope, F.D.: Air pollution and economic growth in dubai a fast-growing middle eastern city. 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Sustainability 14(2), 606 (2022)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Infrastructure resilience, Preparedness, Institutional confidence, AI trust, Dubai","lastPublishedDoi":"10.21203/rs.3.rs-8490604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8490604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban resilience in rapidly growing cities requires more than technical infrastruc- ture solutions, it depends on public awareness, institutional trust, and acceptance of emerging technologies. Dubai provides a critical case where climate stress, demographic diversity, and a Smart City agenda converge, a reality underscored by the April 2024 extreme rainfall event that caused widespread flooding and transport disruptions across the city. This study examines resilience perceptions in Dubai, integrating hazard awareness, preparedness, institutional confidence, and trust in AI-enabled systems. Results show that visible hazards such as floods and traffic disruptions dominate risk perception, while slower-onset threats like groundwater depletion remain underestimated. Preparedness is strongly corre- lated with institutional confidence but is significantly lower among expatriates. While AI-driven resilience solutions attract support, trust in these technologies is conditional and hinges on oversight, transparency, and inclusivity. The findings highlight three interlinked vulnerabilities, fragmented hazard perception, uneven preparedness, and fragile trust in institutions and technologies. From these, five principles are proposed to guide resilience strategies, broaden hazard awareness, make preparedness mandatory, embed transparency in governance, establish safe- guards for AI, and ensure inclusivity across demographic groups. Beyond Gulf studies that emphasize infrastructure or governance capacity, this study provides empirical evidence on how demographic divides, institutional trust, and attitudes toward emerging technologies shape resilience in Dubai. Beginning with a public perception survey as the first stage of a broader mixed methods design, it offers a foundation that will inform subsequent Delphi consultations and resilience planning in hyper arid, fast-growing cities across the Gulf and wider Global South.\u003c/p\u003e","manuscriptTitle":"Infrastructure Resilience in Hyper-Arid fast-growing Cities: Public Perceptions, Institutional Confidence, and AI Trust in Dubai","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 14:01:59","doi":"10.21203/rs.3.rs-8490604/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1c8a509f-2ef5-45f3-aba6-59f3499b51c8","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-08T14:26:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 14:01:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8490604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8490604","identity":"rs-8490604","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}