1. INTRODUCTION
Climate extremes are defined as the occurrence of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends of the range of observed values of the variable (Camuffo et al. 2020). Current approaches to assessing how neighbourhoods and buildings adapt to climate extremes, especially extreme events, remain limited. Studies on extremes and climate resilience have long focused on understanding the drivers of adaptive capacity in urban contexts, mainly at a city or regional scale, but rarely at the neighbourhood scale (Ye & Niyogi 2022). The need for examining the neighbourhood has been called for by many scholars (Aurigi 2024; Cowie et al. 2015; Joshi et al. 2024) but hampered by methodological issues with current approaches that are overly reliant on either urban analytics that capture high-level data or building-level studies.
Research on urban analytics for climate adaptation frequently draws on large-scale datasets such as census records, national statistics, spatial indicators or remote sensing, applying computational and statistical methods to examine how urban form, infrastructure and socio-economic conditions shape adaptive capacities (Yu & Fang 2023). Such approaches have been particularly useful for benchmarking climate risk and resilience across cities and for identifying typologies of vulnerability, though their resolution often hides neighbourhood-level heterogeneity (Batty 2019; Venerandi et al. 2018). By contrast, building-level models employ techniques such as detailed energy simulation, archetype modelling and post-occupancy evaluation to assess how individual structures perform under climate stress, including extreme heat or energy disruption (Dong et al. 2023). While these methods generate precise technical and operational insights, they are typically too data intensive to scale up effectively.
Between these poles lies a diverse set of approaches for assessing climate adaptive capacity, ranging from vulnerability and resilience assessments to community-based tools, insurance models and sustainability certifications (Singh et al. 2022). While these frameworks identify risks, capacities and infrastructure responses, they often privilege macro-scales, technical measures or hazard-specific modelling. Even with advances in participatory mapping, digital twins and sensor-based monitoring, most bypass the everyday, hyperlocal dynamics of neighbourhood adaptation. Furthermore, most adaptive capacity work to date has been based on secondary desktop data, with limited qualitative work on people’s experiences of extremes in their neighbourhoods and homes.
Policy work recognises the need to connect local context and policy drivers, Scotland’s Adaptation Climate plan (Scottish Government 2024), for example, acknowledges the need to better understand diversity of risk and the variety of responses to extremes; however, there has been little to advance knowledge or ability to dynamically assess neighbourhood adaptive approaches to climate risk and, importantly, what differs from one neighbourhood to another. While the Local Authority Climate Service exists in UK, its focus is primarily on climate change and risk, rather than extremes. In relation to climate extremes, there are limitations: some councils and organisations have developed specific protocols, for example, Oxford’s Event Cancellation Procedure (Oxford City Council 2024) and the National Fire Chiefs Council’s (NFCC) Wildfire, Flooding, and Inland Water Safety Policy Statements (NFCC 2023), as well as broader resilience discussions such as the Royal Society’s (2014) report. However, these are primarily general procedural guidelines rather than tools and emphasise emergency response rather than predictive capabilities. In terms of predictive tools and approaches, the European project U-ADAPT! proposes a method for dynamically measuring urban adaptation to extreme heat (Martín & Paneque 2022). Climate extremes reveal knowledge and methodological gaps at neighbourhood/district scales. This paper addresses these by synthesising multidisciplinary knowledge and proposing a new analytical approach tailored to neighbourhood climate adaptation analysis. It presents a conceptual synthesis combining spatial analytics, environmental indicators, community insights and mixed methods. The findings help planners, policymakers, and designers understand context-specific adaptation processes and variation causes.
2. METHODS
This study employed a scoping review methodology (Peterson et al. 2017) to gather and synthesise the range of knowledge on adaptation to socio-environmental extremes across multiple scales, with particular attention paid to the neighbourhood as a critical, yet underexplored, unit of analysis. The choice of a scoping review was guided by the recognition that research on neighbourhood-level adaptation remains relatively new, fragmented and highly interdisciplinary (Munn et al. 2018), and existing studies are distributed across fields as varied as urban studies, planning, architecture, geography, anthropology, biology, ecology, engineering and computational sciences, with different conceptual framings and methodological approaches. The purpose of this scoping review was exploratory and synthetic, aiming to capture conceptual diversity, trace methodological developments, and identify thematic gaps across a heterogeneous and expanding field by cataloguing what is known about neighbourhood adaptation and clarifying how adaptation to climate risks and extremes has been conceptualised, operationalised and modelled across scales, while highlighting how the meso-level of neighbourhoods is situated between city-wide frameworks and building-level strategies.
The review follows a staged and integrative analytical approach, combining a scoping review with grey literature integration, and subsequent thematic synthesis. The process was informed by established frameworks for scoping reviews to ensure transparency and reproducibility while retaining the flexibility required for an interdisciplinary exploratory study (Arksey & O’Malley 2005; Tricco et al. 2018).
Search strings, database filtering and tracking of records were adopted through successive stages of screening alongside thematic coding, inductive refinement of categories and synthesis of conceptual framings. This integration of quantitative breadth and qualitative depth balances methodological rigour with flexibility, supporting both the mapping of the literature and the development of a conceptual framework for neighbourhood-scale climate risk adaptation.
The review (Figure 1) began with a comprehensive search across four academic databases to ensure broad coverage of environmental sciences, engineering and technology research, and the social sciences, thereby capturing both technical and social dimensions of adaptation. Screening was conducted in two stages: titles and abstracts were first assessed against inclusion and exclusion criteria (Figure 1), followed by full-text reviews of 69 studies evaluating each study’s methodology, disciplinary grounding and analytical scale. These studies were classified as conceptual, empirical or mixed, and further categorised by focus scale, allowing the mapping of methodological diversity and disciplinary distribution across references to produce a refined set of results. The evidence base was then expanded through snowballing (Wohlin 2014), tracing in-text citations to identify influential but initially uncaptured works. Selected grey literature, comprising practitioner frameworks and models from consultancies, non-governmental organisations (NGOs) and policy bodies, was included due to its crucial influence on adaptation practices and its substantive conceptual or methodological contributions identified through targeted searches and reference chaining. The integration of systematic searching, snowballing, and grey literature inclusion brought the total number of sources to 77 (Figure 1).
Figure 1
Process for the selection of studies.
Data were extracted using a structured matrix that documented bibliographic details, geographical focus, disciplinary approach, evidence type, analysis scale and thematic contribution. Thematic categories were inductively developed from the literature, covering adaptation models, urban climate resilience, energy systems, water infrastructure, coastal resilience, analytical approaches, social vulnerability, governance, technology and infrastructure.
Narrative synthesis followed scoping review principles. Thematic analysis identified the similarities and differences within clusters, such as urban resilience methods and occupant behaviour studies. Analysing scale integration revealed that most studies addressed one or two geographical levels, with few considering nested phenomena across city, neighbourhood and building. Limitations include the grey literature, which varies in rigour; language restrictions excluding non-English work; and broad criteria that may dilute the thematic focus.
Despite these limitations, the review’s integrated approach offers a comprehensive, transparent synthesis of knowledge on adaptation to socio-environmental extremes. Combining systematic search, iterative refinement, snowballing and thematic analysis balances rigour with flexibility, underpinning the development of a conceptual framework that holistically analyses climate risk at the neighbourhood scale, encompassing environmental, spatial and social dimensions.
3. FINDINGS
Urban adaptation to climate extremes spans planning, engineering, public health and social sciences, with a growing literature providing models, methods and studies on city responses to frequent compound hazards. These contributions operate across scales from global to neighbourhood and building levels, emphasising technological, social, ecological and governance resilience aspects. Climate risks unevenly impact urban areas as heatwaves, floods and coastal hazards interact with local social, ecological and infrastructural variations. While city-scale models identify broad vulnerabilities, they miss complex neighbourhood interactions among physical form, social networks and governance. Building-focused strategies address immediate risks but neglect community-wide capacities and emergent behaviours. This fragmentation has hindered tools for evaluating neighbourhoods as integrated resilience units.
3.1 KEY THEMES
The review categorises existing research into two principal complex themes: ‘Uses and types of assessment approaches’ and ‘Focus on risk areas’, each representing a distinct but linked knowledge area. Table 1 summarises the themes, insights and references.
Table 1
Summary of themes in the literature review.
| SUBTHEMES | KEY INSIGHTS | REFERENCES |
|---|---|---|
| Uses and types of assessment approaches | ||
| Conceptual and analytical approaches for urban adaptation to compound hazards | Integrating social, ecological and technological dimensions reveals compound risks; systemic interactions and feedback loops are often overlooked in static models | Chang et al. (2021); Chondrogianni & Karatzas (2023); Coppola (2020); Hemmers et al. (2020); Karimi et al. (2018); Khromova et al. (2025); Manton (2010); Mishra et al. (2015); Palanikkumar et al. (2025); Van Westen (2013); Xoplaki et al. (2012) |
| Cross-scale analytical approaches to urban adaptation | City-scale frameworks (e.g. ARUP) provide holistic assessments, but overlook meso-scale dynamics; tools such as U-ADAPT! and ClimaWATCH bridge scales | Arup (2014); Batty (2019); De Wit et al. (2020); Dong et al. (2023); Haddad et al. (2022); Hao & Wang (2022); Keshaviah et al. (2025); Kumar et al. (2022); Lin et al. (2021); Ling (2022); Martín & Paneque (2022); Soomro et al. (2025); Yu & Fang (2023) |
| Interdisciplinary perspectives on adaptive mechanisms | Biological and design analogies, socio-ecological and environmental, infrastructural, economic and social systems to inform adaptive systems thinking | Javanroodi et al. (2023); Ling (2022); Pirasteh et al. (2024); Suleimany & Sulaimani (2025) |
| Empirical versus conceptual models of climate adaptation | Mixed-method approaches combine fieldwork, big data and simulations; empirical validation remains inconsistent | Bag et al. (2022); Connon (2019); Dodd et al. (2024); Lemos et al. (2016); Qudrat-Ullah (2025a); Ramadan et al. (2025); Ried (2021); Soomro et al. (2025) |
| Social vulnerability, community resilience and public health | Social capital, inequalities, perceptions and health shape adaptive capacity; social infrastructure enhances resilience | Adger (2010); Arvin et al. (2025); Connon (2019); Connon & Hall (2021); Dodd et al. (2024); Joshi et al. (2024); Kaloyan et al. (2023); Kirmayer et al. (2009); Lee (2014); Lin et al. (2021); Porter et al. (2014); Qi et al. (2024); Sonta & Jiang (2023); Taylor et al. (2014); Tenzing (2020); Venerandi et al. (2018) |
| Governance, policy and adaptive management | Adaptive governance frameworks address uncertainty: barriers to planned adaptation persist; behavioural science can inform climate policy | Coppola (2020); Kaloyan et al. (2023); Moser & Ekstrom (2010); Rijke et al. (2012); Suleimany & Sulaimani (2025); Urlainis et al. (2022); Wilhite et al. (2014) |
| Technological and infrastructural dimensions of climate adaptation | Internet of Things, social media, and smart tools aid monitoring and adaptation, but risk overreliance and neglect of social context | Bag et al. (2022); Collins et al. (2015); De Wit et al. (2020); Marvin et al. (2013); Otum Ume et al. (2020); Palanikkumar et al. (2025); Pirasteh et al. (2024); Ried (2021); Soomro et al. (2025); Terracciano & Han (2023); Wilhelmi & Hayden (2010) |
| Focus on risk areas | ||
| Urban climate resilience and vulnerability | Vulnerability is unevenly distributed across neighbourhoods; morphology, land use and governance strongly shape resilience outcomes | Arvin et al. (2025); González et al. (2021); Hao & Wang (2022); Javanroodi et al. (2023); Joshi et al. (2024); Kim et al. (2025); Lindley et al. (2006); Qudrat-Ullah (2025b); Ramadan et al. (2025); Venerandi et al. (2018); Wilhelmi & Hayden (2010) |
| Energy systems and resilient cooling | Multilayered resilience frameworks highlight cascading risks; hybrid passive–active cooling strategies enhance adaptive capacity | Al-Assaad et al. (2025); Charani Shandiz et al. (2020); Kumar et al. (2022); Zhang et al. (2021); Zhao et al. (2024) |
| Flood risk and water infrastructure resilience | Technical approaches dominate but often neglect governance and social dimensions; integrated tools (GIS, ML) improve mapping and prediction | Assad & Bouferguene (2022); Chen et al. (2015); Collins et al. (2015); Ramadan et al. (2025); Rasheed et al. (2024); Winter & Karvonen (2022) |
| Coastal resilience and sea-level rise | Hybrid and nature-based solutions combine ecological and engineering benefits; adaptation pathways needed for long-term management | Nicholls (2018); Palanikkumar et al. (2025); Unguendoli et al. (2023); Xu et al. (2025) |
| Forecasting, early warning and disaster communication | Forecasting and early warning systems require integration of technical models with social communication; social media is increasingly critical | Abdel-Mooty et al. (2021); Alexander & Tebaldi (2012); Astitha & Nikolopoulos (2023); Calovi et al. (2023); De Wit et al. (2020); Joshi et al. (2024); Liu et al. (2022); Manton (2010); Mishra et al. (2015); Suleimany & Sulaimani (2025); Travis (2013); Van Westen (2013); Xoplaki et al. (2012) |
Across the two main themes summarised in Table 1, urban adaptation research demonstrates considerable theoretical breadth, but remains methodologically and conceptually fragmented. Conceptual and analytical frameworks addressing compound hazards emphasise the integration of social, ecological and technological systems to capture interconnected urban risks, yet often rely on static representations that insufficiently account for dynamic interactions and feedback loops (Chang et al. 2021; Chondrogianni & Karatzas 2023; Hemmers et al. 2020; Karimi et al. 2018; Khromova et al. 2025; Van Westen 2013). Early resilience studies link urban form with social vulnerability, while more recent work foregrounds structural inequality, accessibility and systemic constraints as central determinants of adaptive capacity (González et al. 2021; Hao & Wang 2022; Lindley et al. 2006; Wilhelmi & Hayden 2010).
Cross-scale analytical approaches further reveal tensions between holistic city-scale assessments and the need to capture spatial heterogeneity. City-wide frameworks and urban analytics offer comprehensive diagnostic tools, but tend to mask neighbourhood-level variability (Arup 2014; Batty 2019; De Wit et al. 2020; Dong et al. 2023). In contrast, meso-scale tools such as U-ADAPT! and ClimaWATCH bridge household-level exposure and wellbeing with broader urban systems, reinforcing evidence that resilience emerges across interacting scales rather than residing at a single level of analysis (Haddad et al. 2022; Javanroodi et al. 2023; Keshaviah et al. 2025; Lin et al. 2021; Martín & Paneque 2022).
The risk-focused literature further highlights uneven adaptation outcomes across urban systems. Studies of energy systems and resilient cooling demonstrate how cascading risks arise from multilayered infrastructures and their interaction with urban form and social conditions (Charani Shandiz et al. 2020; Javanroodi et al. 2023; Zhang et al. 2021). Flood risk research shows that advances in geographical information system (GIS), remote sensing and machine-learning have strengthened hazard mapping and prediction, yet governance and social dimensions remain underrepresented in many technical assessments (Chen et al. 2015; Collins et al. 2015; Hemmers et al. 2020; Ramadan et al. 2025). Coastal adaptation studies similarly emphasise hybrid engineering and nature-based strategies while stressing the need for long-term adaptive pathways to manage sea-level rise (Nicholls 2018; Xu et al. 2025).
Across these domains, social vulnerability, public health and governance research consistently demonstrate that adaptive capacity is shaped less by the availability of technologies than by inequalities, institutional arrangements and social infrastructure (Adger 2010; Connon 2019; Connon & Hall 2021; Dodd et al. 2024; Moser & Ekstrom 2010; Qi et al. 2024; Rijke et al. 2012; Suleimany & Sulaimani 2025). Forecasting and early warning studies further confirm that predictive accuracy must be coupled with trusted communication and socially embedded warning systems to support effective adaptation (Alexander & Tebaldi 2012; Astitha & Nikolopoulos 2023; Liu et al. 2022; Terracciano & Han 2023). Taken together, the two themes described in Table 1 indicate that urban adaptation science has expanded across methods and risk areas, yet assessment remains either too coarse to capture neighbourhood-scale differentiation or too narrow to reveal systemic interactions, leaving the neighbourhood as a critical but underdeveloped meso-scale for operationalising urban resilience.
3.1.1 Uses and types of assessment approaches
Conceptual and analytical approaches for urban adaptation to compound hazards
Urban adaptation research increasingly recognises the need to address compound urban risks, yet the analytical approaches employed vary in how they operationalise interaction, uncertainty and scale. Conceptual frameworks such as those by Karimi et al. (2018) and Coppola (2015) are effective at structuring vulnerability across social and environmental dimensions, but remain analytically limited by their static and composite treatment of risk, offering little insight into dynamic interactions or feedback processes. Climate-focused analyses strengthen understanding of temporal variability and extremes, yet largely externalise urban systems, treating cities as passive recipients of climatic forcing rather than adaptive, interacting systems (Manton 2010; Mishra et al. 2015; Xoplaki et al. 2012).
Operational system frameworks attempt to overcome these limitations. In the Social–Ecological–Technological Systems (SETS)-based assessment described by Chang et al. (2021) and Khromova et al. (2025), vulnerability analysis explicitly acknowledges social, ecological and technological interdependencies; however, their reliance on indicator-based aggregation constrains the representation of non-linearity and causal feedbacks, reducing systemic interaction to comparative metrics. Similarly, GIS-based multi-hazard approaches improve spatial explicitness and exposure mapping, but conceptualise interaction primarily through co-location, rather than through cascading or adaptive dynamics (Van Westen 2013).
More explicitly, systemic perspectives shift the analytical focus toward governance, institutional coupling and decision-making under complexity, offering stronger theoretical engagement with feedbacks and hierarchy (Chondrogianni & Karatzas 2023; Hemmers et al. 2020). Yet these approaches often sacrifice empirical operability and quantification, limiting their applicability for comparative assessment or implementation. Data-driven frameworks further expand analytical capacity by integrating multiple hazards and urbanisation pressures, but do so at the cost of interpretability and causal transparency (Palanikkumar et al. 2025).
Overall, the literature reveals a persistent analytical trade-off: approaches that are conceptually rich in system dynamics tend to be weakly operational, while those that are empirically strong often reduce complexity to static indicators or spatial overlays. This tension underscores the continued need for urban adaptation frameworks that can reconcile systemic interaction, uncertainty and empirical usability without collapsing compound risk into simplified proxies.
Cross-scale analytical approaches to urban adaptation
Urban resilience research increasingly employs cross-scale approaches that connect buildings, neighbourhoods and cities, recognising that resilience emerges from interactions across scale. These approaches differ in resolution, methods and focus: strategic frameworks, such as Arup’s City Resilience Framework (CRF), operate primarily at the city scale, assessing systemic, social, institutional, infrastructural and environmental dimensions, but often smoothing over neighbourhood-level variation (Arup 2014). Urban analytics perspectives emphasise cities as complex systems that capture emergent behaviours across scales using computational and spatial methods (Batty 2019). Data-driven models bridge scales differently: CLARITY integrates climate projections with urban and neighbourhood planning (De Wit et al. 2020), while building stock models aggregate micro-scale building performance to inform district- and city-scale decision-making (Dong et al. 2023).
At finer scales, empirical and simulation-based methods link micro-scale conditions to macro-scale outcomes. Building-level studies demonstrate how thermal performance affects occupant vulnerability (Haddad et al. 2022) and adaptation strategies (Kumar et al. 2022), while cellular automata simulations explore how neighbourhood structure influences city-wide accessibility under extreme events (Hao & Wang 2022). Behavioural data and real-time tools, such as social-media heatwave detection, connect individual actions to city-scale hazard assessment (Soomro et al. 2025). Tools such as U-ADAPT! and ClimaWATCH operate at the meso-scale, integrating multidimensional indicators and large datasets to assess neighbourhood-level adaptation while informing city-level strategies (Keshaviah et al. 2025; Martín & Paneque 2022).
Despite methodological differences, these approaches are similar in their approach linking processes across scales: they integrate physical, social and infrastructural dimensions to translate micro-level dynamics into city-scale insights. By spanning building, neighbourhood and city scale, they highlight that urban resilience is not a feature of a single scale, but the outcome of interactions among multiple levels, enabling targeted interventions that are simultaneously locally relevant and systemically coherent (Lin et al. 2021; Yu & Fang 2023).
Interdisciplinary perspectives on adaptive mechanisms
Adaptive mechanisms are increasingly studied through interdisciplinary approaches that integrate design, socio-ecological, environmental, infrastructural, economic and social systems to inform adaptive systems thinking. In the built environment, design-led and biomimetic strategies enhance resilience to extreme climatic conditions at building and neighbourhood scales (Ling 2022). Urban energy adaptation is further explored through socio-technical transitions theory, emphasising the interdependencies between urban morphology, climate dynamics and energy infrastructure in shaping climate-resilient systems (Javanroodi et al. 2023). Environmental and socio-ecological dimensions of adaptation are addressed through spatially explicit modelling and remote sensing approaches that assess multi-hazard exposure and vulnerability within complex socio-ecological systems (Pirasteh et al. 2024). Beyond biophysical systems, adaptive capacity is shaped by socio-political and economic factors, as demonstrated through spatio-temporal analyses of extreme heat vulnerability that integrate exposure sensitivity and adaptive capacity across national contexts (Suleimany & Sulaimani 2025).
Empirical versus conceptual models of climate adaptation
Research on climate adaptation assessment has made significant progress in articulating robust conceptual frameworks, with scholars emphasising that clear definitions of vulnerability, resilience and adaptive capacity are essential to avoid reductionist measurement (Connon 2019; Lemos et al. 2016). Qualitative synthesis and multi-source approaches further strengthen this conceptual grounding by capturing social structure, lived experience and behavioural dynamics often missed by purely quantitative models (Dodd et al. 2024; Soomro et al. 2025). At the same time, recent empirical innovations, including big-data analytics, GIS-based vulnerability mapping and remote-sensing approaches, demonstrate the field’s growing technical capacity to operationalise adaptation concepts (Bag et al. 2022; Ramadan et al. 2025). However, despite these advances, empirical applications frequently assume rather than test the validity of underlying concepts, resulting in operational outputs that are weakly comparable across contexts. As a result, validation is often framed as triangulation or methodological richness rather than as systematic cross-context testing. This persistent conceptual–empirical gap has been explicitly acknowledged by Ried (2021) and increasingly critiqued as a constraint on the cumulative development of adaptation science, with calls for stronger validation frameworks that balance contextual sensitivity with generalisability (Qudrat-Ullah 2025a).
Social vulnerability, community resilience and public health
Social structures and health strongly influence adaptive capacity. Adger (2010) identifies social capital, collective action, and behavioural differences as foundational to resilience and household adaptation (Porter et al. 2014; Taylor et al. 2014). Lee (2014) proposes social vulnerability indicators to identify disparities in disaster impacts, Connon (2019) highlights that young in-migrants are a vulnerable category, but agency matters (Connon & Hall 2021). Dodd et al. (2024) synthesise how health impacts from climate extremes are mediated by inequalities and institutional support. Qi et al. (2024) demonstrate how public space design can reinforce community resilience.
Governance can mitigate or deepen disparities. Kaloyan et al. (2023) show how behavioural science can inform climate policy design, while Sonta & Jiang (2023) examine how social infrastructure underpins adaptive capacity. Venerandi et al. (2018) and Arvin et al. (2025) explain how urban form and socio-spatial inequality interact to produce uneven adaptation outcomes. Joshi et al. (2024) link governance arrangements directly to community vulnerability, while Kirmayer et al. (2009) underscore the importance of cultural and psychological dimensions of resilience.
Governance, policy and adaptive management
Governance remains both an enabler and a barrier. Moser & Ekstrom (2010) propose a diagnostic framework for identifying barriers, Rijke et al. (2012) advocate for ‘fit-for-purpose’ governance that embraces uncertainty and context-specific adaptation, while Wilhite et al. (2014) suggest policies focused on risk reduction rather than responses.
Behavioural science offers insights into governance. Kaloyan et al. (2023) demonstrate how behavioural frameworks can improve climate policy, while Suleimany & Sulaimani (2025) look at governance adaptation and climate policy. Coppola (2015) stresses the centrality of governance in coordinating systemic adaptation, and Urlainis et al. (2022) point to disaster management institutions as critical nodes of adaptive governance.
Technological and infrastructural dimensions of climate adaptation
Technology provides tools but risks overreliance. Wilhelmi & Hayden (2010) illustrate the value of combining automated systems with humans, while Collins et al. (2015) caution that technology often neglects social dimensions. Bag et al. (2022) show how big-data analytics improve resilience, while Soomro et al. (2025) demonstrate the potential of deep learning and social media for real-time hazard detection.
Digital and infrastructural innovations are transforming adaptation. Marvin et al. (2013) examine smart city transitions, while De Wit et al. (2020) demonstrate how digital platforms can support adaptation. Otum Ume et al. (2020) and Pirasteh et al. (2024) show how the Internet of Things (IoT) and remote-sensing technologies can enhance monitoring of environmental risks, while Palanikkumar et al. (2025) highlight their integration into hybrid adaptation frameworks. Terracciano & Han (2023) reveal the communicative role of technology. Ried (2021) emphasises the ongoing challenge of aligning technological solutions with institutional capacity.
3.1.2 Risk areas
Urban climate resilience and vulnerability
Resilience is complex, shaped by morphology, governance and socio-spatial inequalities. Lindley et al. (2006) introduce morphological units for heat mapping, in neighbourhood level assessment, a framework extended by Wilhelmi & Hayden (2010) that links people and place, demonstrating that social vulnerability intersects with physical exposure to heat. González et al. (2021) identify systemic barriers to resilience, while Hao & Wang (2022) show that decentralised mixed-use urban forms improve accessibility and resilience; Javanroodi et al. (2023) stress energy–morphology interactions to shape adaptive capacity, and Kim et al. (2025) show that blue–green infrastructure can mitigate urban heat island effects.
Governance and socio-spatial inequalities remain critical. Qudrat-Ullah (2025b) stresses the role of community engagement, while Ramadan et al. (2025) combine GIS and remote sensing to map flood risk, urbanisation and topography, and other studies highlight how inequality–form interactions’ governance can produce or mitigate uneven vulnerabilities (Arvin et al. 2025; Joshi et al. 2024; Venerandi et al. 2018).
Energy systems and resilient cooling
Energy resilience is vital under heat extremes. Charani Shandiz et al. (2020) propose a multilayered framework integrating engineering, operational and societal dimensions, while Zhao et al. (2024) trace vulnerabilities in the water–energy–carbon nexus. At the building and neighbourhood scales, cooling strategies are key. Zhang et al. (2021) find that passive–active methods are most effective at enhancing resilience quantify the benefits of passive cooling strategies, highlighting the importance of resilient energy infrastructure (Al-Assaad et al. 2025; Kumar et al. 2022).
Flood risk and water infrastructure resilience
Flood resilience has been dominated by technical models and reveals the necessity of integrating governance and social factors. Chen et al. (2015) provide a spatial multicriteria framework, but Collins et al. (2015) warn that overlooking social vulnerability and structural inequalities misrepresent the risk. Assad & Bouferguene (2022) and Winter & Karvonen (2022) highlight governance gaps in struggling with institutional complexity, leading to risks of maladaptation. These gaps are slowing being addressed. Rasheed et al. (2024) apply machine learning to forecast flood peaks, while Ramadan et al. (2025) integrate GIS and multicriteria decision-making to map vulnerability in arid cities.
Coastal resilience and sea-level rise
Coastal zones require hybrid approaches balancing ecological and engineering strategies. Nicholls (2018) advocates multistep adaptation pathways that allow for flexibility. Unguendoli et al. (2023) model nature-based solutions: these can be cost-effective and ecologically beneficial. By reviewing hybrid approaches, Xu et al. (2025) note both technical challenges and social barriers to implementation, while Palanikkumar et al. (2025) highlight the compounded risks in coastal cities undergoing rapid urbanisation.
Forecasting, early warning and disaster communication
Forecasting faces integration challenges. Alexander & Tebaldi (2012) highlight difficulties in modelling extremes and their societal impacts, while Travis (2013) reviews early-warning-system design. Van Westen (2013) underscores the role of GIS and remote sensing in multi-hazard assessment, and Abdel-Mooty et al. (2021) extend this incorporating long-term recovery considerations.
Astitha & Nikolopoulos (2023) provide an overview of urban forecasting, including wildfire risks, whereas Calovi et al. (2023) stress integrating social communication strategies into forecasting models. Liu et al. (2022) show that public perceptions shape uptake of warning systems, emphasising trust and information granularity. Suleimany & Sulaimani (2025) examine forecasting in fragile contexts, while Mishra et al. (2015) and Xoplaki et al. (2012) emphasise climatic uncertainty, and Manton (2010) calls for adaptive forecasting under shifting climate baselines.
Social media has become a key communication channel. Terracciano & Han (2023) reveal, through Twitter activity during extreme events, both real-time risk perception and institutional communication gaps. Joshi et al. (2024) further demonstrates how governance shapes disaster communication effectiveness.
3.2 PROPOSED ANALYTICAL APPROACH
The analytical approach proposed in this study responds to a clear gap in the climate adaptation literature: while cities have been conceptualised as complex socio-ecological systems, and buildings as sites of micro-level technical and behavioural interventions, the neighbourhood has remained analytically underdeveloped despite its evident importance. Previous analytical approaches, such as the Arup’s CRF (Arup 2014), have offered invaluable city-scale diagnostics, enabling policymakers to assess resilience across dimensions, such as health, infrastructure and governance. Yet these approaches tend to generalise local heterogeneity into aggregated indicators, obscuring the subtle variations in risk and capacity that play out across different neighbourhoods within the same city. Conversely, building-focused studies, including the thermal modelling of Haddad et al. (2022) or the cooling interventions assessed by Al-Assaad et al. (2025), capture the material and behavioural specifics of adaptation, but rarely account for collective capacities that emerge once households are linked through shared social interactions, infrastructure, morphology and governance.
The proposed analytical approach (Figure 2) positions the neighbourhood as the critical meso-scale at which resilience is shaped and expressed, conceptualising it as an adaptive and emergent system rather than merely a collection of households or a miniature version of the city. At this scale, infrastructures, social networks, cultural practices, and governance structures intersect, interact and co-evolve. Focusing on the neighbourhood reveals dynamics that remain obscured at purely macro- or micro-scales, including patterns of technology adoption, morphological influences on hazard exposure and forms of community agency in climate response.
Figure 2
Proposed analytical approach framing neighbourhoods as adaptive systems.
Figure 2 conceptualises the neighbourhood as an emergent, dynamic system embedded in interacting social, ecological, technological and governance processes. This is represented by a central circular element explicitly annotated as ‘emergent, interacting systems monitored over time’, emphasising that adaptive capacity is continually reshaped through feedback loops rather than fixed at a single point in time. Bidirectional arrows highlight that neighbourhood dynamics both influence and are influenced by broader system interactions.
This systems-based and temporal conceptualisation directly informs the integration of diverse methodologies within the analytical approach. Spatially explicit analyses, including GIS-based modelling, support the examination of urban morphology, infrastructure distribution and hazard exposure (Chen et al. 2015; Ramadan et al. 2025), while participatory interviews and qualitative methods capture lived experience, local knowledge and community agency that shape neighbourhood-level adaptive capacity (Connon 2019). These are complemented by big-data-driven analytical tools, represented in Figure 2 by ClimaWATCH and the monitoring functions of U-ADAPT!, which enable near real-time and longitudinal assessment of environmental and social dynamics (Ried 2021; Soomro et al. 2025). The feedback loops indicate that insights generated through these heterogeneous methods are iteratively integrated over time, reflecting a deliberate methodological pluralism and recognising that no single dataset or technique can adequately capture the multidimensional and dynamic nature of adaptive capacity.
Surrounding the central approach are four complementary frameworks and tools that inform and operationalise the analytical approach. The SETS framework provides the systemic foundation, foregrounding interactions across social, ecological and technological domains, and highlighting that vulnerability and resilience arise through their interdependencies rather than within isolated sectors (Chang et al. 2021; Khromova et al. 2025). U-ADAPT! is positioned as a multidimensional indicator system for monitoring adaptation progress. Its explicit bidirectional and circular arrows, labelled ‘dynamic adaptation’, reinforce the understanding that assessment, learning and adjustment are iterative processes, consistent with forecasting research that conceptualises resilience as emergent through feedbacks and thresholds (Alexander & Tebaldi 2012; Astitha & Nikolopoulos 2023; Martín & Paneque 2022).
ClimaWATCH represents the synthesis of environmental and social data for near real-time vulnerability assessment. Its connection to the neighbourhood through curved arrows labelled ‘monitoring over time’ foregrounds the longitudinal dimension of the analytical approach, emphasising continuous reassessment as conditions, exposures and social capacities evolve (Keshaviah et al. 2025).
Arup’s CRF provides strategic principles for resilience planning. The added annotation ‘governance & collective action’ makes explicit the role of institutional capacity, coordination and agency, clarifying that governance is an active and constitutive component of neighbourhood adaptability rather than a background condition (Arup 2014).
Overall, the explicit feedback loops and temporal cues reinforce the core logic of the analytical approach: neighbourhood resilience is not located within individual domains or tools, but emerges through ongoing interaction, learning, and adjustment across systems and scales. The neighbourhood is a pivotal meso-scale where city-wide strategies, technical assessments and lived experience intersect.
This conceptual synthesis (Figure 2) improves adaptation by guiding action, and highlighting how vulnerabilities across social, ecological, technological and governance systems reveal leverage points for targeted interventions. It frames green infrastructure as boosting social cohesion, reducing energy demand and encouraging governance. Strengthening energy systems also depends on affordability and governance. This approach offers a way to understand resilience variations and tailor locally grounded and systemically coherent interventions.
It surpasses earlier generic or narrow models by viewing neighbourhoods as adaptive systems, capturing resilience as it unfolds and acts as both an analytical tool and a guide for policymakers and practitioners to create context-sensitive, systemic interventions. This approach holds the potential to operationalise the complexity across themes, moving beyond compartmentalisation to a dynamic understanding of urban climate resilience.
4. DISCUSSION AND CONCLUSIONS
This study advances the debate on climate adaptation by explicitly positioning the neighbourhood as the critical meso-scale for both assessing, understanding and potentially predicting resilience. While the literature to date has focused on either macro-city-scale frameworks or micro-building-level interventions, the findings show that these approaches, though valuable, leave a methodological and conceptual gap. The proposed multimodal analytical approach addresses this gap by recognising that neighbourhoods are not merely aggregates of buildings or reduced-scale cities, but systems of locally relevant adaptation in which spatial form, social practices and infrastructural arrangements interact dynamically.
What distinguishes this analytical analysis from its predecessors is not only its scope but also its integrative and systemic design aspects. Earlier models tended to treat social, ecological, technological and governance factors as parallel dimensions to be assessed separately. The multimodal approach instead conceptualises these domains as co-constitutive and mutually shaping. For instance, social vulnerability is never considered in isolation: it manifests through its entanglement with infrastructural exposure, ecological conditions and governance structures. Khromova et al. (2025) demonstrate that social fragility is decisive in shaping hydrological risks, but those vulnerabilities gain force precisely when coupled with inadequate infrastructures or fragmented governance, as Winter & Karvonen (2022) illustrate. Similarly, ecological assets such as green infrastructure, evaluated by Kim et al. (2025), do not function independently: their effectiveness depends on spatial placement, institutional support and community use. Consistent with the analytical approach presented in Figure 2, resilience is located not within individual domains but at their interaction.
Integration also allows the proposed approach to move beyond static conceptions of resilience that treat adaptive capacity as a fixed attribute. Drawing on forecasting research by Alexander & Tebaldi (2012) and Astitha & Nikolopoulos (2023), the model conceptualises resilience as emergent, continually reshaped by feedback loops, thresholds and context-specific interactions. Adaptive systems such as neighbourhoods cannot be conceptualised as static; rather, their capacity to absorb and transform under stress must be continually monitored and adjusted. Here, tools such as U-ADAPT! (Martín & Paneque 2022) and ClimaWATCH (Keshaviah et al. 2025) play a crucial role in enabling iterative, near-real-time tracking of vulnerabilities and responses across scales, allowing neighbourhoods to dynamically reconfigure in response to evolving risks. A key advancement of the proposed approach lies in integrating social, ecological, technological and governance systems not as discrete pillars, but as interacting processes embedded within the neighbourhood scale. This extends beyond the diagnostic capacity of SETS frameworks (Chang et al. 2021; Khromova et al. 2025), enabling both the identification of vulnerability hotspots and the design of targeted interventions. By integrating diverse methodologies ranging from GIS-based spatial modelling (Chen et al. 2015; Ramadan et al. 2025) or participatory interviews (Connon 2019) and big-data-driven analytical tools (Ried 2021; Soomro et al. 2025), the proposed approach embodies methodological pluralism. It explicitly recognises that no single dataset or technique can capture the multidimensional and dynamic nature of adaptive capacity.
By framing neighbourhoods as adaptive systems, this approach advances resilience analysis in three ways. First, it shifts from static adaptive capacity assessments to emergent resilience shaped by feedback loops across social, ecological, technological and governance systems, addressing compounding shocks, cascading failures and uneven climate risk impacts. Second, it emphasises contextual resilience outcomes varying by morphology, infrastructure, governance and community agency, necessitating fine-grained, place-based assessment over city-wide generalisations. Third, it employs methodological pluralism integrating quantitative spatial analytics, qualitative community insights, and participatory approaches to capture complexity and develop actionable practitioner tools.
Analytically, it bridges city–building-scale gaps by operationalising resilience at the ‘middle range’ where daily life and collective action occur, advocating social–physical infrastructure over isolated interventions. Practically, it equips planners/policymakers with diagnostic strategies to target context-specific interventions (infrastructure upgrades, social networks, governance adjustments) avoiding one-size-fits-all approaches, while enabling design practitioners to embed resilience into neighbourhood planning/regeneration aligned with local capacities. Adaptation emerges as a socially embedded process driven by community agency and interdependencies beyond technical metrics.
Limitations include the scoping review’s breadth without empirical testing across diverse urban contexts; future research should validate through case studies with real-time data, community engagement and longitudinal monitoring under uncertainty.
In conclusion, this multimodal analytical approach conceptualises climate resilience at the neighbourhood scale by interacting social–ecological–technological–governance dimensions. Positioning neighbourhoods as meso-scale interaction/feedback hubs enables nuanced assessment, informed anticipation and locally grounded interventions, expanding systemic yet context-sensitive policy/practice toolkits for resilient futures.
COMPETING INTERESTS
The authors declare that they have no known competing financial or personal interests that could have appeared to influence the work reported in this paper.
DATA ACCESSIBILITY
The data that support the findings of this study are available from the corresponding author upon reasonable request.