Introduction
Coastal regions, particularly coastal cities, are integral to providing essential ecosystem services as well as economic and cultural benefits (Ferro-Azcona et al., 2019). Nevertheless, these areas are characterized by high population densities and are significantly influenced by climate change and various human activities. In this context, the concept of adaptive capacity - defined as the capability of systems, institutions, and individuals to adjust to potential damages, capitalize on emerging opportunities, or respond to the consequences of change (Whitney et al., 2017) - has become a critical focus of research. However, assessing and strengthening adaptive capacity in coastal socio-ecological systems (SES) is a complex challenge due to rapid environmental change and the variety of socio-economic and ecological factors (Whitney et al., 2017).
Along with resilience and vulnerability, adaptive capacity is a crucial concept applied to understanding how SES respond to change (Gallopín, 2006). Numerous studies indicate that adaptive capacity is not an independent attribute of a social-ecological system but is closely related to, and sometimes overlaps with, the concepts of resilience and vulnerability. Ferro-Azcona et al. (2019) highlighted that although adaptive capacity and resilience are distinct concepts, they are mutually related: adaptive capacity is an attribute of social-ecological resilience, enabling the system to transition when the current state is unsustainable, while resilience encompasses the adaptation needed to maintain functionality over time.
SES is not simply the combination of social and ecological systems; rather, it is unified and interconnected systems marked by robust interactions and feedback loops both within and across social and ecological elements that influence its overall behavior (Biggs et al., 2021). However, most studies reveal the separation of assessment units for SES adaptive capacity, either social adaptive capacity (e.g., assets, capitals, governance) or ecological adaptive capacity (e.g., genetic diversity, phenotypic plasticity) (Ferro-Azcona et al., 2019; Whitney et al., 2017; Espinoza Córdova et al., 2024). This lack of connection or integration can lead to “pitfalls”: optimizing social adaptability can degrade ecological adaptive capacity, leading to the long-term collapse of the entire system (Whitney et al., 2017). This study, therefore, aims to synthesize recent studies on adaptive capacity addressing SES in the context of worldwide coastal cities, thereby bridging theoretical and practical gaps. Moreover, the objective of this study is also to identify emerging issues in human-environment interaction and to recognize how indigenous knowledge and culture contribute to the adaptive capacity and resilience of coastal cities to environmental changes.
Research Methods
This study employs a rapid review design to synthesize evidence on how adaptive capacity is assessed through the lens of SES for coastal cities. As defined by Grant and Booth (2009), rapid reviews utilize systematic review methods to search for and critically appraise existing research but streamline specific steps to assess knowledge on policy or practice issues. In a rapid review, the search strategy can utilize limited resources but remaining explicit, and the research question is defined as priori using the PICOC framework (Population, Intervention, Comparison, Outcome, and Context) to emphasize the socio-environmental context (Eyzaguirre and Fernandes, 2024). Notably, this study utilizes the PICOC framework not only to define the search strategy but also to extract information from retrieved documents, which serves as input for the subsequent thematic analysis.
Formulating research questions
The main goal of this study is to synthesize how recent empirical studies assess the adaptive capacity of coastal cities through the SES lens. Following the PICOC framework, specific research questions are as follows:
1. Population: What specific coastal populations and SES components are the focus of adaptive capacity assessments?
2. Intervention and Comparison: What methodological frameworks and approaches are used to assess adaptive capacity in coastal SES, and what are their key differences regarding theoretical and practical foundations?
3. Outcomes: What indicators are used to measure adaptive capacity, and what common barriers, challenges, and limitations are identified in these assessments? What are the spatio-temporal scales of assessment?
4. Context: What contextual factors lead to adaptive capacity assessment?
Literature search and selection
A review of peer-reviewed literature was performed on December 1, 2025, using the Scopus database. The included studies met all three keyword groups - coastal cities, adaptive capacity, and social-ecological systems - in their titles, keywords, and abstracts. Regarding adaptive capacity, only one of the following keywords should be met: “adaptive capacity”, “adaptation”, “vulnerability”, “resilience”, or “robustness”. Refulio-Coronado et al. (2021) highlighted that while resilience, vulnerability, and adaptive capacity are three concepts used to understand how SES responds to change, robustness emphasizes the critical role of engineered components in SES adaptation. For SES, interchangeable search terms were “social-ecological system”, “socio-ecological system”, “socioecological system”, or “human-environment”. The query generated a list of 16 studies, seven of which were excluded because they did not assess adaptive capacity, did not adopt the SES approach, or were not located within a coastal city. As a result, nine studies were included in data extraction and thematic analysis based on the framed research questions.
Data extraction and analysis
The Excel sheet was used to extract and compile data, code the keywords, and generate the themes. For each study, the extracted and compiled data include general research information - such as authors, publication year, keywords, objectives, methods, findings, and recommendations - as well as specific details related to the research questions, like SES’s adaptive capacity conceptualizations, assessment frameworks, scales, and indicators. Table 1 presents the final selected studies in the order by publication year.
To synthesize findings from the selected heterogeneous dataset - including quantitative index-based studies, qualitative participatory assessments, and system dynamics modeling - this study employed a qualitative thematic analysis approach. Given the complexity of SES, a rigid meta-analysis was unsuitable; instead, a qualitative thematic analysis enabled the identification, analysis, and reporting of patterns (themes) across diverse analytical frameworks (Ahmed et al., 2025).
The analysis followed a three-stage hybrid process which combined deductive and inductive reasoning. First, a deductive coding framework was applied to map the definitions of core concepts (adaptive capacity, resilience, vulnerability, social-ecological systems) against established theoretical lineages. Second, an inductive approach was used to allow emerging themes to surface directly from empirical evidence, specifically regarding the spatial and temporal scales of assessment or the trade-offs in adaptive governance. Finally, the coded data was collated into overarching themes to answer the research questions. This approach critically synthesized how adaptive capacity operates across contexts, revealing tensions and methodological shifts that are often overlooked in narrative reviews.
Results and Discussion
Typical characteristics of adaptive capacity assessment
Table 1 indicates that the majority of the reviewed studies were published from 2020 onward, which is consistent with the findings reported by Kanan and Giupponi (2024). In addition to the impacts of global climate change, coastal cities in compiled studies are exposed to both individual and cumulative effects, including sea-level rise, changes in the frequency and intensity of tropical storms, rapid degradation of marine and coastal ecosystems, and uncontrolled but rapid urban development. Around the globe, Southeast Asia faces high disaster risks due to its dense population and low-lying terrain. However, this study reveals a notable lack of publications assessing adaptive capacity through the lens of the SES approach in this region, as highlighted by Kanan and Giupponi (2024).
From the perspective of SES, the reviewed publications show a wide variety of conceptual frameworks related to adaptability. It includes different assessment methods, from quantitative to qualitative and mixed approaches, as well as static indicators that reflect current conditions and dynamic models that predict future scenarios (Table 2). Furthermore, the reviewed studies examine adaptive capacity across multiple spatial scales in coastal urban contexts. Integrating insights from diverse geographical settings, the thematic analysis clarifies how coastal cities are shifting from incremental adaptation toward more proactive and transformative approaches.
Assessment framework: From static index to dynamic model
The dominance of static index assessment
The methodological landscape of adaptive capacity assessment is dominated by two distinct yet complementary approaches: static index-based assessments and dynamic modeling frameworks. The most common approach static index is the use of indicator-based methods shaped by risk-hazard vulnerability and resilience assessment framework (Paterson et al., 2017; Tauzer et al., 2019; Marín-Monroy et al., 2020; Olivares-Aguilar et al., 2022; Ambily et al., 2024). This method is appropriate for various scales of assessment, from household (Marín-Monroy et al., 2020) and community levels (Tauzer et al., 2019) to city (Ambily et al., 2024, Paterson et al., 2017) and regional levels (Olivares-Aguilar et al., 2022). This methodological preference is driven by the need for simplified, comparable measures that guide policy allocation and identify hotspots of vulnerability.
At the household level, Marín-Monroy et al. (2020) employed Principal Component Analysis (PCA) to develop a Socio-ecological Vulnerability Index, which encompasses the three dimensions of exposure, sensitivity, and adaptive capacity, for households in Baja California Sur, Mexico. Based on this assessment, the studied municipalities fell into either highly, moderately, or less vulnerable scenarios. In alignment with the risk-hazard vulnerability framework, adaptive capacity is conceptualized as a vital determinant of the vulnerability index; at the household scale, this is typically measured through social capital and education for natural hazard prevention, which are essential for facilitating collective action and social learning. As a result, it provided a valuable framework for managers to allocate emergency support resources effectively and plan for highly vulnerable irregular settlements in coastal regions.
Expanding the scope of assessment, Olivares-Aguilar et al. (2022) employed a vulnerability framework—comprising exposure, sensitivity, and adaptive capacity—to evaluate the climate vulnerability index across five coastal regions in Venezuela. The findings demonstrated that adaptive capacity is operationalized through a diverse suite of interventions, including community awareness, the structural resilience of coastal infrastructure, transformative livelihoods, and adaptive governance. Significantly, this study marks a methodological shift from earlier household-level assessments in Mexico by transitioning to a city-regional scale and adopting a spatially explicit social-ecological approach. This evolution allows for the analysis of cumulative environmental exposures rather than isolated risks, providing a more robust evidence base for regional adaptation planning.
At the urban infrastructure and community scale, Ambily et al. (2024) implemented the Ecological Flood Resilience Index (EFRI), grounded in socio-ecological resilience theory, to evaluate the adaptive transformation of Kochi City, India. The composite EFRI captures three critical ecological dimensions: (1) capacity and resourcefulness, (2) diversity and composition, and (3) the connectivity of Blue-Green Infrastructure (BGI). Within this adaptation pathway, BGI serves as a transformative ecological intervention that mitigates pluvial flood risks while enhancing human-nature interaction. However, the results indicated a significant spatial decline in BGI resilience toward the highly urbanized core, where urban sprawl and siltation have severely compromised natural drainage. Critically, as noted in Table 3, while the EFRI provides high-resolution data on ecological connectivity, it exemplifies a technocentric limitation by focusing solely on drainage processes and excluding socio-economic variables, thereby failing to provide a holistic and generalized assessment of adaptive capacity within a complex social-ecological system.
Focusing on the community and periurban scale in southern Ecuador, Tauzer et al. (2019) argued that adaptive capacity is fundamentally shaped by subjective community perceptions and experiences. These cognitive factors are critical as they dictate how individuals engage with formal early warning systems and risk management interventions. While the study builds on the traditional framework of livelihood capitals - comprising human, natural, financial, physical, and social assets - it advances the methodology by integrating qualitative participatory approaches, such as focus groups, alongside quantitative government datasets. This hybrid method revealed that adaptive capacity is not uniform but exhibits significant spatial and temporal variations. Such disparities are driven not only by a lack of material resources but also by deficiencies in social organization and political involvement, suggesting that at the community level, adaptive capacity is a dynamic product of both tangible assets and intangible social-political cohesion. The emphasis on subjective perceptions at the community scale highlights a critical gap in purely technical assessments, suggesting that resilient urban futures require a synergy between community-based social capital and macro-level governance structures.
Expanding to the institutional and organizational scale, Paterson et al. (2017) utilized the Adaptive Capacity Index (ACI) - grounded in structuration theory work on shadow systems - to emphasize the role of social structures and organizations within government, civil society, and private entities involved in adaptive planning and management. This framework moves beyond material assets to evaluate internal processes (learning and adaptive management) alongside operational functions (risk identification and mitigation). A critical finding of this comparative analysis is the emergence of the ‘scale trap,’ where smaller administrative units are burdened with decentralized adaptation responsibilities without a corresponding transfer of financial or human resources Consequently, these smaller cities often bypass congested formal channels by relying on “shadow systems” - informal mechanisms and personal networks that maintain adaptive capacity in the absence of institutional support. This highlights that at the city-institutional level, adaptive capacity is as much about the flexibility of informal social structures as it is about formal planning protocols.
Addressing the need for a transdisciplinary and multi-sectoral perspective, Tu et al. (2022) integrated the Systems Approach Framework (SAF) with the Drivers-Pressures-States-Impacts-Responses (DPSIR) framework to develop an adaptive management plan for Rongcheng, China. This study implemented a seven-step participatory process that facilitated co-learning among diverse stakeholders, including local government, research institutions, and coastal practitioners. Unlike static index-based approaches that often overlook the human-nature interface, this co-designed governance model explicitly identifies and ranks environmental stressors while simultaneously navigating the inherent trade-offs between economic development (e.g., aquaculture) and the preservation of cultural and ecological heritage. The findings suggest that at the city-regional scale, adaptive capacity is not merely a technical output but a negotiated process of aligning multi-sectoral priorities to avoid fragmented governance and ecological degradation.
In summary, static index-based assessments serve as a critical entry point for coastal governance by simplifying complex social-ecological realities into comparable and actionable metrics. The review of these studies reveals a strategic methodological evolution across spatial scales: from quantifying household assets and education in Mexico to mapping the connectivity of blue-green infrastructure at the city scale in India, and finally to co-designing multi-sectoral management plans in China. However, the dominance of this “snapshot” approach (Table 3) often masks the “scale trap” or “rigidity trap” where decentralized adaptation responsibilities at the local level are not matched by formal institutional resources, forcing smaller cities to rely on informal “shadow systems” to maintain their adaptive capacity. While these indices are highly effective for identifying vulnerable hotspots and guiding immediate policy allocation, they remain inherently limited by their “additive” logic, which often fails to capture the non-linear feedback loops and “recovery pitfalls” characteristic of complex adaptive systems (CAS) - one of the most critical properties of SES (Biggs et al., 2021). This fragmentation between ecological performance and socio-economic dynamics underscores the urgent necessity of transitioning from static “asset-counting” toward the dynamic behavioral modeling explored in the following section.
The shift towards dynamic modelling approaches
While static indices provide a necessary baseline for immediate resource allocation, the inherent volatility and non-linearity of coastal environments necessitate a transition toward dynamic modeling. This shift signifies more than a methodological change; it represents a deeper ontological evolution, viewing coastal social-ecological systems (SES) not as collections of stable states, but as complex adaptive systems (CAS) characterized by uncertainty, feedback loops, and emergent behaviors.
Key works include Oliveira et al. (2022), which demonstrates the use of System Dynamics to assess adaptability and resilience, and Chen et al. (2025), which enhances this ‘dynamic’ perspective by analyzing long-term panel data to identify non-linear relationships. In these models, adaptive capacity is defined as the SES’s ability to maintain the provision of a desired set of ecosystem services (such as fishery yields or water quality) in the face of shocks and gradual change. This capacity is formed through the interaction between ecological variables (e.g., biomass regeneration rate) and social governance principles, including diversity, learning capacity, and participation. Furthermore, adaptive capacity is determined through the lens of feedback loops, nonlinearity, and path dependence. Therefore, in a dynamic model, adaptive capacity is not merely viewed as a static asset reserve, but rather as a behavioral attribute of the system that evolves over time.
Utilizing a Cobb-Douglas production function integrated with seven resilience principles, Oliveira et al. (2022) evaluated the Dynamic Resilience Index (DRI) for 10 ecosystem service types in Ubatuba, Brazil, through the MIMES framework. A pivotal finding of this research is the “seasonality” of SES resilience, demonstrating that adaptive capacity is not a fixed attribute but fluctuates throughout the year. This variation is driven by “slow variables”, such as economic cycles and water quality, allowing managers to strategically prioritize interventions during “highly adaptable seasons” while preparing for “vulnerable seasons”. Furthermore, the model identifies the system’s position within the adaptive cycle -comprising exploitation, conservation, release, and reorganization. Notably, during the conservation stage, high connectivity within the coastal SES can unexpectedly lead to a rigidity trap, where practical adaptive capacity decreases despite static indicators suggesting system strength.
Additionally, Chen et al. (2025) utilized longitudinal panel data covering 49 Chinese coastal cities over a 23-year period. Their analysis identified an N-shaped non-linear relationship between economic development (GDP) and Marine Protected Areas (MPAs), which serve as proxies for ecological adaptive capacity. This research highlights critical thresholds where economic growth initially reduces adaptive capacity before eventually promoting it, illustrating that the human-environment interaction is neither linear nor static.
In summary, dynamic models address the fundamental “why” and “when” questions of adaptation, offering a safeguard against recovery pitfalls that occur when systems appear robust in static indicators but are actually approaching a tipping point. However, the rigorous data requirements and computational complexity of these models - as summarized and highlighted in Table 3 - suggest that the more effective path forward lies in an integrated assessment framework that synergizes the spatial precision of static indices with the process-oriented depth of dynamic simulations.
Advancing an integrated framework for assessing adaptive capacity
No single disciplinary lens can fully capture the multi-dimensional complexity of coastal social-ecological systems (SES). While quantitative, expert-led methodologies provide rigorous analyses of causal relationships, they often overlook hard-to-quantify socio-cultural factors—a limitation particularly evident when viewing coastal-urban SES as dynamic complex adaptive systems (CAS) characterized by non-linear feedback and transition thresholds. Conversely, participatory qualitative frameworks excel at integrating indigenous knowledge or co-production from multi-stakeholders and building consensus but may struggle with cross-regional generalization.
As summarized in Table 3, the fundamental divergence between these methods lies in their ontological basis: static index-based approaches treat adaptive capacity as a beneficial asset reserve, whereas dynamic modeling conceptualizes it as a behavioral attribute that enables system adjustments over time. Acknowledging this distinction, we argue that the most robust assessment outcomes are achieved through an Integrated Assessment Model (IAM). This framework synergizes the spatial precision of static indices with the temporal depth inherent in coastal dynamics. For instance, integrating explicit spatial data with temporal simulations facilitates modeling how the “seasonality of adaptive capacity” is experienced across an urban network, encompassing both peripheral zones and adjacent metropolitan cores.
Furthermore, participatory dynamic modeling offers a pathway to incorporate qualitative data on cognition and informal “shadow systems” into structured models. By using insights from focus groups to parameterize feedback loops, researchers can ensure that local perspectives are preserved rather than obscured by mathematical abstractions, thereby avoiding the rigidity traps often found in top-down management.
In conclusion, a comprehensive assessment of adaptive capacity in coastal cities requires a strategic synthesis of methodologies ranging from static indicators to dynamic simulations. The ability of static spatial data to map hotspots under data-deficient conditions allows for a shift from merely identifying “who” and “what” are vulnerable to precisely locating “where” social-ecological systems are under the greatest pressure. Simultaneously, dynamic modeling addresses the “why” of system change and the “when” of intervention effectiveness, providing a critical safeguard against recovery pitfalls that static methods might overlook.
However, the current lack of a unified approach remains a challenge. Future research must prioritize the development of an integrated, dynamic framework that bridges the gap between technical expertise and stakeholder knowledge. Only through such a transformative shift in assessment can we foster a more-than-human urbanism - one that is resilient, inclusive, and capable of navigating the non-linear challenges of climate change in the 2026–2045 period and beyond.
Urban scale and the spatial disparities of adaptive capacity
The urban scale - defined by both administrative hierarchy and the extent of urbanization - exerts a profound influence on adaptive capacity, creating systemic inequalities in how cities navigate transformative adaptation. The administrative tier of a city is a primary determinant of its access to the financial and technical resources essential for climate resilience. By comparing three distinct levels of urbanization - Selsey (small town), Santos (medium city), and Broward County (regional level) - Paterson et al. (2017) demonstrated that disparities in adaptive capacity are often more pronounced across administrative levels (from central to local) than between the public and private sectors. Smaller cities frequently fall victim to a form of “unfunded decentralization”, where responsibility for adaptation is shifted to the local level without a corresponding transfer of budget or expertise, leaving them isolated in national policy dialogues.
In this “scale trap”, small and medium-sized cities often rely on informal mechanisms, or “shadow systems”, to maintain operational capacity. These personal networks and informal data-sharing channels become vital lifelines when formal institutional pathways are congested or non-existent. However, this reliance on informality signals a deeper vulnerability. As theorized by Carpenter and Brock (2008), such systems are at risk of falling into social-ecological rigidity traps - where institutions become highly connected yet inflexible - or social-ecological poverty traps, where the loss of innovation and resources eventually impairs the system’s ability to respond to shocks, leading to a new and degraded state.
Moreover, the economic scale and geographic position (center vs. periphery) dictate the choice of adaptation strategies. Ekman (2023) argues that wealthy, central hubs (e.g., Copenhagen, Jakarta) often pursue “resistance” strategies by investing in high-cost engineering like seawalls. Conversely, peripheral cities (e.g., Malé, Tarawa) are forced toward “flexible adaptation” or “managed retreat” due to resource constraints. This is empirically supported by the N-shaped model of Chen et al. (2025), which identifies a critical economic threshold (GDP > 55 billion RMB) required for effective investment in ecological adaptive capacity; below this, development pressures typically over-shadow conservation efforts.
Medium-sized cities and rapidly urbanizing peri-urban areas face a unique “resilience paradox”. As noted by Ambily et al. (2024) in Kochi and Marín-Monroy et al. (2020) in Los Cabos, concentrated development often degrades the very green infrastructure that provides flood protection, pushing vulnerable populations into underserved suburban fringes. In Machala (Ecuador), the absence of formal early warning systems forced communities to develop informal information channels via social media. While these “shadow systems” demonstrate remarkable community resourcefulness, their lack of institutional stability poses a long-term risk to the city’s overall sustainability.
Synthesizing a path towards sustainable and adaptive urban coastal futures
The evaluation of adaptive capacity through integrated static and dynamic lenses has moved beyond simple categorization, revealing a complex landscape of scale-dependent traps and spatial disparities. These findings indicate that for coastal cities - particularly small and medium-sized ones - achieving long-term sustainability is not a matter of increasing “assets” alone but of reconfiguring the system’s behavioral logic to ensure that adaptive capacity itself is sustainable. Based on the diagnosis of structural barriers like the scale and rigidity traps, two interconnected pathways emerge to secure a resilient future for coastal-urban social-ecological systems (SES).
Human-centered spatial transition: Bridging the governance gap in small and medium-sized coastal cities
The identified scale trap (Paterson et al., 2017) and the resulting resilience paradox in rapidly urbanizing cores (Ambily et al., 2024) demand a transition that prioritizes institutional continuity. This pathway is vital for smaller administrative units where resources are often decoupled from responsibilities through.
Formalizing local resilience
In small and medium-sized cities, “shadow systems” (informal networks) are often the only functioning mechanism for adaptation when formal channels fail. To ensure the sustainability of adaptive capacity, these informal networks must be transitioned into a supported, funded multi-level governance model. This prevents the isolation of peripheral communities and ensures that local knowledge is not just a temporary fix but a permanent pillar of the city’s resilience.
Preventing poverty traps
By ensuring that decentralized responsibilities in smaller cities are matched with technical and financial resources, planners can prevent the loss of innovation. This transition secures a “minimum floor” of adaptive capacity, stopping the resource depletion that leads to degraded SES states and ensuring that smaller cities are not left behind in the national adaptation agenda.
More-than-human urbanism: Breaking the rigidity trap through co-evolution
To address the rigidity traps and recovery pitfalls exposed by dynamic modeling (Oliveira et al., 2022; Chen et al., 2025), a deeper ontological shift is necessary to ensure that adaptation does not lead to long-term stiffness. This pathway seeks to:
Empower ecological persistence
Using indices like the EFRI provides “empirical counter-data” that allows managers in medium-sized cities to challenge the anthropocentric bias toward hard infrastructure. This ensures that Blue-Green Infrastructure (BGI) is recognized as a non-negotiable strategic asset. By protecting these natural buffers, cities can maintain a sustainable adaptive capacity that does not rely on costly, high-maintenance engineering.
Implementing ecotone dynamics
Shifting from static zoning to permeable, co-evolutionary designs—as seen in the Mekong Delta Region Plan (Ekman, 2023) - allows the urban fabric to “breathe” alongside shifting biophysical parameters. This approach replaces rigidity with flexibility, ensuring that even as medium-sized cities grow, they remain integrated with their coastal environment rather than becoming “locked-in” to vulnerable, fixed-boundary structures.
The synergy between these two pathways allows for a transformation from “who is vulnerable” to “how to adapt sustainably”. By integrating the spatial visibility of static indices with the mechanistic depth of dynamic models, coastal cities - regardless of their size - can navigate the non-linear challenges of future coastal development. This synthesized approach ensures a future that is as flexible, inclusive, and persistent as the ecosystems upon which it depends, fostering an adaptive capacity that is both socially equitable and ecologically sound.
Conclusion
This rapid review has navigated the complex evolution of adaptive capacity (AC) assessment within coastal urban social-ecological systems (SES), tracing the shift from static “asset-counting” to dynamic behavioral modeling. By synthesizing diverse methodologies, the study demonstrates that achieving long-term sustainability - particularly for small and medium-sized coastal cities - requires moving beyond the mere accumulation of resources toward a fundamental reconfiguration of the system’s behavioral logic. The primary contribution of this research is the identification of structural pathologies, such as scale, rigidity, and poverty traps, which often remain obscured in traditional, fragmented assessment frameworks. The proposed Integrated Assessment Framework offers a strategic response to these challenges by synergizing the spatial precision of static indices with the mechanistic depth of dynamic simulations. To secure a sustainable and adaptive future, coastal governance must embrace two interconnected pathways: (1) a human-centered spatial transition that resolves administrative inequalities and unfunded decentralization in smaller cities, and (2) a more-than-human urbanism that replaces rigid engineering with co-evolutionary and ecotone-based designs. At last, fostering future resilience depends on the ability to maintain an adaptive capacity that is both socially equitable and ecologically persistent, ensuring that coastal cities can breathe and evolve alongside their shifting biophysical environments.
While this study offers a novel synthesis of social-ecological traps and adaptive pathways, it acknowledges several limitations inherent in its design, addressed here to provide a transparent foundation for future inquiry:
Methodological scope and sample size
Consistent with the principles of a rapid review, this study utilized a purposive sampling of nine high-impact, methodologically diverse studies. While this sample size limits statistical generalization, it was prioritized to ensure thematic saturation and a deep methodological analysis of the traps and pathways within SES. Future research could expand this scope through a full systematic review or meta-analysis to validate the universality of these structural traps across a broader range of administrative tiers.
Geographic and economic focus
The reviewed literature predominantly reflects regions with established research infrastructures. The applicability of the Integrated Assessment Framework in highly resource-constrained coastal regions of the Global South -where “shadow systems” are the primary mode of survival - requires further empirical validation, particularly in the context of emerging medium-sized cities.
Integration of emerging technologies
This review focuses on the transition from static to dynamic modeling but does not fully explore the potential of real-time Big Data, Digital Twins, or Artificial Intelligence in enhancing the “liveliness” of urban adaptive capacity. Investigating how these technologies can be integrated into participatory adaptive governance remains a critical frontier for smart and resilient coastal development.
Socio-psychological dimensions
The current analysis emphasizes institutional and ecological structures. A vital avenue for future research is the investigation of perception-action gaps and environmental identity at the community level. Understanding how local stakeholders perceive their own adaptive capacity is essential to ensuring that formal interventions do not unintentionally trigger “rigidity traps” by stifling local innovation.
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