Traditional coastal flood risk prediction often overlooks critical geographic features, underscoring the need for accurate risk prediction in coastal cities to ensure resilience. This study enhances the prediction of coastal flood occurrence by utilizing the Geospatial Artificial Intelligence (GeoAI) approach. This approach employed models—random forest (RF), k-nearest neighbor (kNN), and artificial neural networks (ANN)—and compared them to the IPCC risk framework. This study used El Salvador as a demonstration case. The models incorporated seven input variables: extreme sea level, coastline proximity, elevation, slope, mangrove distance, population, and settlement type. With a recall score of 0.67 and precision of 0.86, the RF model outperformed the other models and the IPCC approach, which could avoid imbalanced datasets and standard scaler issues. The RF model improved the reliability of flood risk assessments by reducing false negatives. Based on the RF model output, scenario analysis predicted a significant increase in flood occurrences by 2100, mainly under RCP8.5 with SSP5. The study also highlights that the continuous mangrove along the coastline will reduce coastal flood occurrences. The GeoAI approach results suggest its potential for coastal flood risk management, emphasizing the need to integrate natural defenses, such as mangroves, for coastal resilience.

Currently, urban vulnerability has been exposed by catastrophic and unpredictable events which required cities to improve their resilience. Urban farming is promoted as one of the alternative strategies that could improve resilience through community empowerment aligned with re-naturing the environment. This study highlights the role of urban farming as community empowerment activities which could develop community resilience in the context of food and nutrition security specifically as emergency response. The study utilized an in-depth field survey to develop the database. The study found that urban farming could contribute to community resilience for feeding potential and nutrient sufficiency especially for targeted population who has highest risk during emergency such as the COVID-19 case. Urban farming in Malang could feed up to 50,000 inhabitants which cover only an age range of 60-64 years old. To provide sufficient vegetables for targeted population, there was a need for 1.91% (211 ha), 1.09% (120 ha), and 0.82% (91 ha) of area given each production scenarios such as normal, medium, and intensive management, respectively. The most important nutrient needs were Vitamin B1, B2, B12, D; Niacin eq.; and Folic acid due to only 10% sufficiency in the average. The study recommended specific additional vegetables to be cultivated such as red spinach and long beans since they have the highest nutrients content based on scoring.

Non-technical summary: Cities, as complex systems, are faced with increasingly diverse and connected challenges across social, economic, environmental, and health domains. To help cities address these challenges, the Future Earth Urban Knowledge-Action Network developed a cross-disciplinary urban research agenda through expert elicitations and extensive consultation. Five research themes to guide urban sustainability research were identified including: (1) advancing urban sustainability transformations, (2) ensuring equity, (3) boosting innovation in low to lower-middle income countries, (4) managing complexity and systemic risks, and (5) navigating environmental change. Advancing this agenda will require collaboration across disciplines and geographies, transdisciplinary coproduction, and enhanced support to urban science. Technical Cities and urban regions are at the forefront of transformations toward global sustainability. As urbanization accelerates, there is increasing demand for cities to play multiple, complex and synthetic roles across social and environmental domains within and beyond their boundaries, for example driving economic development while mitigating and adapting to global environmental changes. To help cities in meeting this challenge, urban science, a rapidly growing field that includes inter- and transdisciplinary research, needs to expand and evolve, with clear priorities. Combining expert elicitation and community consultation, the Future Earth Urban Knowledge-Action Network developed a strategic research agenda for urban science for the next decade. The urban science research agenda describes five critical research themes for scientific advances: (1) accelerate urban sustainability transformations, (2) ensure equity and inclusivity, (3) amplify innovation from the low to lower-middle income countries, (4) negotiate complexity and systemic risks, and (5) navigate environmental change. Under each research theme, we review the state of the art, identify remaining gaps, and outline key research questions needing to be addressed to advance science toward urban transformations. Interconnections across, and enabling conditions to advance, these priority research themes are discussed. Social media summary: Globally co-designed urban research agenda reveals pressing priorities for sustainability and resilience.

Compound flood (CF) represents a complex hazard that often leads to severe impacts. CF propagates across interconnected systems, generating systemic societal and environmental risks, particularly in coastal cities. Despite progress in data science and remote sensing, a comprehensive review of coupled hydrodynamics with the data-driven GeoAI—an integration of geospatial analysis and artificial intelligence (AI)—for systemic CF risk remains scarce. This review summarizes foundational data-driven and numerical approaches in CF modeling. It then synthesizes emergence, utilization modes, and advancements of coupled hydrodynamic-GeoAI frameworks for CF prediction and systemic impact quantification. A systematic review follows the PRISMA protocol, examining 403 articles from the Web of Science and Scopus databases. The concept of the coupled hydrodynamics-GeoAI model synergizes physics-based simulations with data-driven computational learning, enhancing predictive accuracy and spatially detailed flood risk while explicitly embedding geographic features into the framework. The model offers three utilization modes: (i) direct coupling, (ii) surrogate modeling, and (iii) stochastic statistical-hydrodynamic-ML framework. To enhance comprehensive and robust risk assessment, the review proposes four key model advancements: (1) implementing an active learning framework, (2) integration with physics-guided data-driven, (3) dynamically coupling CF drivers with external factors, and (4) incorporating spatiotemporal analysis under changing climate and socioeconomic conditions. We further advocate for integrating the quantification of both tangible and intangible cascading impacts into systemic CF risk assessments. This review synthesizes computational strategies integrating physics-based hydrodynamics with GeoAI, providing a foundation for systemic CF risk evaluation and guiding future advances in computational hydrology and resilient urban flood management. Graphical Abstract This graphical abstract visually encapsulates the core concept of leveraging coupled hydrodynamic with data-driven GeoAI models for systemic compound flood (CF) risk evaluation in coastal urban areas. The abstract integrates the three main flood drivers—coastal, pluvial, and fluvial—interacting to form the CF. The diagram flows from left to right, where the first section depicts the urban coastal zone, followed by a combination of flood drivers that illustrate the complexity and interconnectedness of factors leading to CF. On the right panel, the graphical focus shifts to the “Coupled Hydrodynamic with Data-Driven GeoAI Model” as the proposed approach for predicting the CF event, encompassing probabilistic analysis, flood propagation, risk assessment, real-time forecasting, and emergency response. The approach integrates physics-based simulations with data-driven geographic data analysis, known as GeoAI. On the bottom left panel, the illustration emphasizes the utilization of the approach through three key strategies: (i) direct coupling through ML-derived boundary conditions, (ii) surrogate modeling to emulate hydrodynamic outputs, and (iii) stochastic statistical-hydrodynamic-ML framework. These strategies demonstrate how GeoAI enhances hydrodynamic simulations to produce more accurate, timely, and spatially detailed flood predictions in various ways. The model’s advancements are also highlighted, addressing the critical need for the utilization of an active learning framework, integration with physics-guided data-driven, dynamically coupling CF drivers with external factors, and the importance of considering spatiotemporal analysis under climate and socioeconomic change. The graphic in the bottom right panel displays the CF output results from the model, which can be further utilized to assess the cascading impact on various aspects. Together, the elements of this graphical abstract convey a sophisticated, interdisciplinary approach to CF risk evaluation, focusing on integrating hydrodynamic with data-driven GeoAI models to better manage the complex challenges of systemic CF risks in urban coastal zones.

Nature-based Solutions (NbS) have emerged as a transformative approach to addressing climate change, biodiversity loss, and sustainable development. Despite growing global recognition, the implementation of NbS remains uneven, particularly in countries like Indonesia, which possess high ecological potential but face several challenges. This study critically reviewed the global recognition and implementation progress of NbS in achieving international sustainability and climate agendas, with a particular focus on identifying key enabling factors and hindering risks, and deriving globally informed lessons tailored to Indonesia’s context. This study presents a comprehensive systematic review of 219 peer-reviewed articles from the Web of Science (WoS) and Scopus, conducted following the PRISMA 2020 protocol. The review identifies three primary typologies of NbS—Intrinsic, Hybrid, and Artificial—each associated with specific ecosystem types and functions. It maps their alignment with major global agendas, including the Sustainable Development Goals (SDGs), the Paris Agreement, Nationally Determined Contributions (NDCs), the Sendai Framework, the Nature-Positive 2030 initiative, the Kunming-Montreal Global Biodiversity Framework (GBF), and the Climate Resilient Development Pathway (CRDP). Intrinsic NbS, such as forests, wetlands, and peatlands, dominate the literature due to their critical roles in carbon sequestration, biodiversity conservation, and disaster mitigation. Findings reveal a significant research gap in Indonesia, with only four studies explicitly linking NbS to national climate commitments of the NDC. Key barriers to implementation include fragmented governance, lack of standardized frameworks, limited stakeholder engagement, and insufficient financial mechanisms. Conversely, enabling factors such as regulatory reforms, green financing instruments (e.g., green sukuk, ecological fiscal transfers), and the integration of traditional ecological knowledge (TEK) offer promising pathways for scaling up NbS. This study contributes a novel typological framework and a synthesis of enabling and hindering factors contextualized for Indonesia. It underscores the need for localized, evidence-based NbS strategies that are aligned with global frameworks yet tailored to national socio-ecological realities. By bridging global lessons with local contexts, the findings provide a strategic foundation for policymakers, researchers, and practitioners to enhance the effectiveness, scalability, and equity of NbS in achieving climate resilience and sustainable development. Graphical Abstract This graphical abstract provides a concise and visually engaging summary of the study, which systematically reviews the global and Indonesia-specific progress of Nature-based Solutions (NbS) in achieving climate and sustainability goals. The visual begins by categorizing NbS into three typologies—Intrinsic, Hybrid, and Artificial—each illustrated with a representative short description to distinguish their ecological and engineered characteristics. The central panel outlines the systematic literature review process, using the PRISMA framework, and highlights the screening and selection of 219 articles from the Web of Science (WoS) and Scopus databases. A four-key aspect is being reviewed concerning various ecosystem types. On the right panel, the abstract further maps the alignment of NbS with major global agendas, including the Sustainable Development Goals (SDGs), the Paris Agreement, Nationally Determined Contributions (NDCs), the Sendai Framework, Nature-Positive 2030, the Kunming-Montreal Global Biodiversity Framework (GBF), and the Climate Resilient Development Pathway (CRDP). These are depicted through recognizable icons, reinforcing the relevance of NbS in international policy frameworks. At the bottom panel, a timeline and global map illustrate the temporal trends and geographical spread of NbS implementation. The findings reveal an underrepresentation of Indonesian contexts in the implementation of NbS. A chart at the corner summarizes the key enabling factors (e.g., stakeholder engagement, financial incentives) and hindering risks (e.g., regulatory barriers, fragmented governance). This visual synthesis highlights the pressing need for localized, evidence-based strategies to scale up NbS in Indonesia. By identifying typologies, ecosystem types, and global lessons learn of key enabling or hindering risks, the study offers actionable insights for researchers, practitioners, and policymakers. It aims to bridge knowledge gaps, inform national climate strategies, and enhance Indonesia’s contribution to global sustainability and climate resilience targets through NbS implementation.