Urban Resilience (UR) enables cities and communities to optimally withstand disruptions and recover to their pre-disruption state. There is an increasing number of interdisciplinary studies focusing on conceptual frameworks and/or tools seeking to enable more efficient decision-making processes that lead to higher levels of UR. This paper presents a systematic review of 68 Scopus-indexed journal papers published between 2011 and 2022 that focus on UR. The papers covered in this study fit three categories: literature reviews, conceptual models, and analytical models. The results of the review show that the major areas of discussion in UR publications include climate change, disaster risk assessment and management, Geographic Information Systems (GIS), urban and transportation infrastructure, decision making and disaster management, community and disaster resilience, and green infrastructure and sustainable development. The main research gaps identified include: a lack of a common resilience definition and multidisciplinary analysis, a need for a unified scalable and adoptable UR model, margin for an increased application of GIS-based multidimensional tools, stochastic analysis of virtual cities, and scenario simulations to support decision making processes. The systematic literature review undertaken in this paper suggests that these identified gaps can be addressed with the aid of asset and disaster risk management methods combined with GIS-based decision-making tools towards significantly improving UR.

This study investigates the intersection of digital transformation, business resilience, and marketing capabilities, focusing on small businesses and startups. The digital revolution has significantly transformed business operations, supply chain management, and overall organizational performance. Conducted following PRISMA guidelines, this systematic literature review used the Scopus database, refining an initial 247 documents to 51 relevant studies. Key trends include the vital role of digital transformation in enhancing resilience, the use of emerging technologies for sustainable supply chains, and the importance of digital skills and knowledge management. Research highlights the implications of digital marketing and e-commerce adoption for SMEs, revealing the need for firms to develop dynamic capabilities to thrive in turbulent environments. However, gaps remain, such as understanding the long-term impacts of digital transformation, the interactions between digital maturity, innovation, and sustainability, and the necessity for comparative studies across industries and regions. Additionally, investigating how marketing capabilities contribute to resilience is essential, enabling small businesses and startups to withstand and recover from disruptions. Addressing these trends and gaps will enhance our understanding of digital transformation’s multifaceted implications for SMEs and startups, helping them leverage marketing capabilities to navigate challenges and seize opportunities in the digital era.

Climate change threatens urban areas globally. Enhancing resilience is crucial, yet the comprehensive clustering of practical climate adaptation measures for use in construction industry decision-making is notably absent. This study investigates and categorizes various climate adaptation measures, justifying each through a lens of risk management, asset management, and previous scientific work. It takes advantage of the innovative digital platform Netobra, which offers an ecosystem for the construction industry, to provide real-world, practical implications of these measures. Informed by the Urban Resilience Evaluation System, ISO 31000 (risk management), and ISO 55000 (asset management), the study sets out to demonstrate the value of these measures in bolstering urban resilience and improving decision-making in the construction industry. Moreover, the study integrates a hotspot detection mechanism for areas at high risk of climate impacts, using multicriteria decision analysis (MCDA)—analytic hierarchy process (AHP) mapping on Netobra. These identified hotspots and corresponding climate adaptation measures will further be incorporated into a Risk-Informed Asset-Centric (RIACT) process, providing valuable insights for climate change mitigation and adaptation strategies in urban development. Through its in-depth analysis, the study aims to contribute to the understanding of how diverse climate adaptation measures can be practically applied in various sectors, thereby enhancing urban resilience and effective risk and asset management.

Floods pose a significant threat to road networks globally, disrupting transportation, isolating communities, and causing economic losses. This study proposes a four-stage methodology (avoidance, endurance, recovery, and adaptability) to enhance the resilience of road networks. We combine analysis of constructed assets and asset system performance with multiple disaster scenarios (Reactive Flood Response, Proactive Resilience Planning, and Early Warning Systems). Advanced flood Geospatial-AI models and open data sources pinpoint high-risk zones affecting crucial routes. The study investigates how resilient assets and infrastructure scenarios improve outcomes within Urban Resilience Index (CRI) planning, integrating performance metrics with cost–benefit analysis to identify effective and economically viable solutions. A case study on the Lisbon Road network subjected to flood risk analyzes the effectiveness and efficiency of these scenarios, through loss and gain cost analysis. Scenario 2, Proactive Resilience Planning, demonstrates a 7.6% increase compared to Scenario 1, Reactive Flood Response, and a 3.5% increase compared to Scenario 3, Early Warning Systems Implementation. By considering asset performance, risk optimization, and cost, the study supports resilient infrastructure strategies that minimize economic impacts, while enabling communities to withstand and recover from flood events. Integrating performance and cost–benefit analysis ensures the sustainability and feasibility of risk reduction measures.

Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap’s road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers.