Amid escalating climate risks and a widening adaptation finance gap, especially in low- and middle-income countries (LMICs), prioritizing adaptation measures for road infrastructure is essential for building sufficient resilience through both public and private adaptation financing. Despite the urgency of the issue, adaptation finance for infrastructure is often hampered by a lengthy and complex decision-making process that involves conflicting stakeholder interests. Existing taxonomies and benchmarks for adaptation finance typically focus on identifying optimal engineering solutions, often overlooking the numerous factors that influence these implementation decisions. To address this gap, this study develops an approach to understand stakeholders’ and decision makers’ preferences for road infrastructure adaptation measures using empirical evidence based on a global survey of 72 out of 129 respondents from industry, academia, and government. Multi-criteria decision analysis is applied using the Best-Worst Method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Four relative importance criteria are assessed, namely, disaster risk reduction, maladaptation risk, adverse societal impact, and project cost, and rank 11 widely-adopted adaptation measures targeting intense precipitation and extreme heat. Results show preventive, cost-effective, and sustainable options, such as improved drainage systems, vegetation plans, and monitoring systems, are consistently favored over capital-intensive structural solutions like alternative road construction and road reallocation. Overall, the findings demonstrate a systematic approach that incorporates diverse stakeholder perspectives into road adaptation planning to enhance the transparency, legitimacy, and implementability of selected adaptation measures.

Complex systems face a myriad of stresses and shocks. The ability of complex systems to deal with such shocks is often expressed in terms of the system’s resilience. Although resilience is conventionally conceptualised as recovery from a single shock or disruption, in reality, systems often face a sequence of successive shocks, as is the case for earthquake aftershocks or epidemic waves. A system’s ability to handle such a sequence of disruptions depends on its capacity to learn from preceding events. To capture adaptation and learning as transformative aspects of resilience, new approaches to account for performance changes across successive disruptions are needed. This paper addresses this gap by developing and empirically testing a novel, performance-based method to quantify adaptation to successive shocks, thereby contributing to the broader discussion on adaptation and resilience. We use the COVID-19 pandemic as a case study which was characterised by multiple waves across different countries and contexts, making it an ideal case to test our theoretical model. We use open-source data from Our World in Data, ranging from 2020 to 2023, and consider every COVID-19 wave as a disruption. Individual waves are identified by fitting mixtures of skew-normal curves to COVID-19 fatality time series, and a performance-based resilience metric, called the Total Performance Loss (TPL), is computed for each wave. Adaptation across successive waves is modelled using an exponential, learning-based metric that captures whether system performance improves or deteriorates over time. The results show that many countries initially showed maladaptive behaviour but adapted in later pandemic waves. The proposed methodology makes adaptation to successive disruptions measurable, and enables systematic comparison of how systems learn, and unlearn over time, which is an essential step towards understanding and improving responses to future crises.

High Impact Low Probability events (HILPs), often referred to as outliers, are becoming more important in disaster management because they are linked to complex risks and tipping points in interconnected systems. Recent events, such as the cascading effects of the coronavirus pandemic, rising uncertainties from global geopolitical instability, and successive and concurrent extremes driven by climate change, underscore the limitations of relying solely on severe but plausible scenarios for risk practitioners and policymakers. Despite the critical need to integrate HILPs into risk assessment models and emergency preparedness, the field is fragmented, with inconsistent definitions and methodologies. We present a perspective developed under the HORIZON AGILE project (AGnostic risk management for high Impact Low probability Events), which introduces two comprehensive definitions of HILPs and a taxonomy designed to enhance risk assessment, resilience analysis, and crisis management. We provide a validated scientific definition for the academic community and an operational definition tailored for practitioners and stakeholders. Additionally, our taxonomy offers a structured framework to address outlier events that often fall below traditional risk thresholds, ensuring that low-probability, high-impact scenarios with cascading and concurrent dynamics are effectively integrated into risk registers, legislation, and standards development. This study shows how this approach improves methods like stress testing and scenario modelling, especially for the loss of critical services. This empowers policymakers, practitioners, and stakeholders to include more scenarios in their strategies, enhancing resilience and preparedness.

On February 6th, 2023, 11 cities around the southeast of Turkey were hit by a major earthquake with magnitude of 7.9 and 7.6, which resulted in mass destruction and loss of lives in many cities. Antakya, Hatay was one of those cities that lost more than 50 % of the built environment and a death toll of over 50,000. Previously known for its profound historical lineage, rich and diverse cultural heritage, and deep-rooted connection to its cultural identity, Antakya's reconstruction process holds a critical case not only for Turkey but also for the global discourse on urban resilience and post-disaster recovery strategies. Thus, in this study, we deep dive into an ongoing recovery process in Antakya, Turkey. The study's primary objective is to identify the key factors, constraints, opportunities, and challenges involved in reconstructing a city that can withstand future earthquakes. The data used in the study was gathered during fieldwork that was carried out in Hatay, Turkey, in June 2023. The methodology consists of total of 16 focus groups and in-depth interviews with stakeholders, including representatives of professional associations, local government agencies, and public institutions. Our results showed that re-opening educational services plays a crucial role in promoting recovery in Antakya. Furthermore, we examined the complex interplay between cultural ties to place, and urban resilience in the context of disaster recovery. We found that in Antakya, cultural identify may play a crucial role and would have a direct influence on its recovery. These findings hold significance for policymakers, urban planners, and disaster management professionals who must navigate the challenging terrain of post-disaster reconstruction while considering the cultural and emotional ties that bind residents to their cities. Ultimately, this research provides valuable insights into the intricacies of urban recovery and reconstruction processes and contributes to the growing body of knowledge on urban resilience and disaster recovery.

As cities grow, the benefits of living in them are increasingly unequally distributed. USA cities, in particular, have experienced rapid suburbanization of poverty and decreased levels of access to jobs for transit-dependent and vulnerable communities. The public transit challenges in suburbs call for innovative forms of transit to turn the tide on urban inequality. On-demand microtransit, a novel type of shared mobility provides efficient, convenient and affordable transportation. Its potential for redressing inequity had yet to be investigated fully in a suburban setting. We presented a case study from the suburbs of Minneapolis-St. Paul in Minnesota, USA. We combined unique datasets of microtransit ridership from two public transit agencies, transit surveys, land use data, and expert interviews, to conduct spatial analysis, accessibility analysis, and equity impact assessments for these suburbs. We found that microtransit enables public transit agencies to reach a larger number of vulnerable riders than fixed-route transit, particularly for commuting and trips to/from commercial areas. Microtransit also provided a cheaper alternative to ride-hailing and a faster alternative to public transit and walking, without cannibalizing ridership from fixed-rout transit alternatives. Finally, microtransit redressed transportation inequities by alleviating access inequality, reaching vulnerable rider groups effectively, and creating travel opportunities that are less spatially concentrated than those provided by traditional, fixed-route public transit. This study provided a framework for further investigations into the impact of microtransit, including in urban core or rural settings, and highlighted the impact of microtransit in reducing access inequity in a suburban environment.

Increasing frequency of climate-related disruptions requires transformational responses over the lifecycles of interconnected urban systems with short- and long-term change dynamics. However, the aftermath of disruptions is often characterised by short-sighted decision-making, neglecting long-term urban shifts. In this study, we present a first attempt to develop the theoretical foundation for temporal dynamics for increasingly disrupted yet ”connecting and moving” cities that can be used in planning for urban resilience. Using the lens of climate urbanism, we conceptualise the interplay of temporal dynamics to empirically examine how planning practice perceives and addresses temporality in two regions - Amsterdam, the Netherlands, and Mumbai, India. Our findings reinforce that disruptions do not inform long-term planning. Endogenous and exogenous dynamics of change are not viewed together nor used to embed short-term planning goals within long-term resilience visions. To address the lack of systematic planning approaches that can leverage temporal dynamics, we propose two options for temporally flexible urban planning processes.

Urban areas are dynamic systems, in which different infrastructural, social and economic subsystems continuously co-evolve. As such, disruptions in one system can propagate to another. However, open challenges remain in (i) assessing the long-term implications of change for resilience and (ii) understanding how resilience propagates throughout urban systems over time. Despite the increasing reliance on data in smart cities, few studies empirically investigate long-term urban co-evolution using data-driven methods, leading to a gap in urban resilience assessments. This paper presents an approach that combines Getis-ord Gi* statistical and correlation analyses to investigate how cities recover from crises and adapt by analysing how the spatial patterns of urban characteristics and their relationships changed over time. We illustrate our approach through a study on Helsinki’s road infrastructure, socioeconomic system and built-up area from 1991 to 2016, a period marked by a major socioeconomic crisis. By analysing this case study, we provide insights into the co-evolution over more than two decades, thereby addressing the lack of longitudinal studies on urban resilience.