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Approximately 11% of the world’s population lives within 10 km of an ocean coastline, a percentage that is likely to increase during the remainder of the 21st century due to urbanization and economic development. In the presence of climate change, coastal communities will be threatened by increasing damages due to sea-level rise (SLR), accompanied by hurricanes, storm surges and coastal inundation, shoreline erosion, and seawater intrusion into the soil. While the past decade has seen numerous proposals for coastal protection using adaptation methods to deal with the deep uncertainties associated with a changing climate, our review of the potential impact of SLR on the resilience of coastal communities reveals that these adaptation methods have not been informed by community resilience or recovery goals. Moreover, since SLR is likely to continue over the next century, periodic changes to these community goals may be necessary for public planning and risk mitigation. Finally, community policy development must be based on a quantitative risk-informed life-cycle basis to develop public support for the substantial public investments required. We propose potential research directions to identify effective adaptation methods based on the gaps identified in our review, culminating in a decision framework that is informed by community resilience goals and metrics and risk analysis over community infrastructure life cycles.

Infrastructure drainage projects play a critical role in urban development but are increasingly exposed to environmental, operational, and climate-related risks that challenge their long-term sustainability. Despite this, decision-makers continue to lack risk-informed, structured methods to assess sustainability performance in an uncertain environment. In order to facilitate evidence-based decision-making and sustainable risk management, this study suggests a risk-informed sustainability index for infrastructure drainage projects. The study first points out a weakness in the methods currently used for sustainability assessments, specifically the lack of risk-sensitive, standardized frameworks designed for drainage infrastructure systems. Altogether, 28 sustainability indicators are identified, with 22 indicators retained after the application of fuzzy set theory criteria. The sustainability index is developed by normalizing, weighting, and combining these indicators using a multi-criteria decision analysis (MCDA) method. To show the usefulness and practicality of the suggested approach in assessing sustainability performance and pinpointing risk-critical improvement areas, it is used for a long-term infrastructure drainage project. In order to improve infrastructure resilience, the findings emphasize the significance of early integration of sustainability and risk considerations, stakeholder engagement, and ongoing performance monitoring. The suggested approach offers a flexible and transferable framework for risk-informed decision-making, assisting engineers, project managers, and policymakers in enhancing the resilience and sustainability of infrastructure drainage systems.

Modern electrical power utilities must deal with the replacement of large portions of their assets as they reach the end of their useful life. Their assets may also become obsolete due to technological changes or due to reaching their capacity limits. Major upgrades are also often necessary due to the need to grow capacity or because of the transition to more efficient and carbon-free power alternatives. Consequently, electrical power utilities are exposed to significant risks and uncertainties that have mostly external origins. In this context, an effective framework should be developed and implemented to maximize value from assets, ensure sustainable operations and deliver adequate customer service. Recent developments show that combining the concepts of asset management and resilience offers strong potential for such a framework—not only for electrical utilities, but for industry, too. Given that the quality and continuity of service are critical factors, the concept of Value of Lost Load (VoLL) is an important indicator for assessing the value of undelivered electrical energy due to planned or unplanned outages. This paper presents a novel approach for integrating the power grid reliability simulator into a holistic framework for asset management and electrical power utility resilience. The proposed approach provides a sound foundation for Risk-Informed Decision Making in asset management. Among other things, it considers asset performance as well as the impact of both current grid topology and customer profiles on grid reliability and VoLL. A case study on a major North American electrical power utility demonstrates the applicability of the proposed methodology in assessing maintenance strategy.

The optimum choice of safety measures (SMs) within constraints is necessary for effective risk management in occupational health and safety (OHS). The stochastic nature of safety interventions is frequently overlooked by traditional approaches such as deterministic models and risk matrices. This study presents a novel stochastic knapsack model that maximizes the overall expected benefit during a risk assessment period considering budgetary constraints and the interdependencies between risks and safety measures. Two models are developed as follows: a one-to-one relationship model assuming independent risks and a multiple-relationship model accounting for interdependent safety measures. The suggested model’s real-world implementation is illustrated through a case study in the retail industry. The results demonstrate the model’s ability to efficiently prioritize SMs, showing an 18% reduction in objective function value and an average risk reduction of 29.5 per monetary unit invested, compared to 26.2 for the deterministic model. A more realistic and flexible framework for safety investment planning is offered by the analysis, which emphasizes the benefits of including stochastic components and interdependencies in decision-making. By addressing the significant drawbacks of deterministic models and providing a flexible, data-driven framework for safety optimization, this study adds to the body of literature. The suggested model is in line with the United Nations Sustainable Development Goals (SDGs), specifically SDGs 3, 8, 9, and 12. Its adaptability contributes to achieving SDG 13, emphasizing possible uses in risk management for climate change. This study shows how decision-making that is structured and aware of uncertainty can support safer, more sustainable industrial processes.

The risk-informed decision-making of metro tunnel project is often faced with the problem of inadequate utilization of available information. In order to address the epistemic uncertainty problem caused by insufficient utilization of information in decision-making, this paper proposes a risk decision support approach for metro tunnel construction based on Continuous Time Bayesian Network (CTBN) technique. CTBN can factor the state space of variables in tunnel projects and perform evidence-based reasoning, which enables the diverse information of expert opinions, project-specific parameters, historical data and engineering anomalies to be the evidence to support decision-making. A concise CTBN model development method based on Dynamic Fault Trees is presented to replace the cumbersome model learning process. The proposed approach can utilize multi-source information as evidence to provide multi-form decision support both in the pre-construction stage and construction stage of the tunnel construction project, and the results can support the decisions on judging the acceptability of the risk, developing response strategies for risk factors and diagnosing the causes of the hazardous event. A case study on the water leakage risk of tunnel construction in China is presented to illustrate the feasibility of the approach. The case study shows that the approach can assist in making informed decisions, so as to improve the engineering safety.

How wildfires are managed is a key determinant of long-term socioecological resiliency and the ability to live with fire. Safe and effective response to fire requires effective pre-fire planning, which is the main focus of this paper. We review general principles of effective federal fire management planning in the U.S., and introduce a framework for incident response planning consistent with these principles. We contextualize this framework in relation to a wildland fire management continuum based on federal fire management policy in the U.S. The framework leverages recent advancements in spatial wildfire risk assessment—notably the joint concepts of in situ risk and source risk—and integrates assessment results with additional geospatial information to develop and map strategic response zones. We operationalize this framework in a geographic information system (GIS) environment based on landscape attributes relevant to fire operations, and define Potential wildland fire Operational Delineations (PODs) as the spatial unit of analysis for strategic response. Using results from a recent risk assessment performed on several National Forests in the Southern Sierra Nevada area of California, USA, we illustrate how POD-level summaries of risk metrics can reduce uncertainty surrounding potential losses and benefits given large fire occurrence, and lend themselves naturally to design of fire and fuel management strategies. To conclude we identify gaps, limitations, and uncertainties, and prioritize future work to support safe and effective incident response.

Across the globe, aircraft that apply water and suppressants during active wildfires play key roles in wildfire suppression, and these suppression resources can be highly effective. In the United States, US Department of Agriculture Forest Service (USFS) aircraft account for a substantial portion of firefighting expense and higher fatality rates compared to ground resources. Existing risk management practices that are fundamental to aviation safety (e.g., routinely asking, “Is this flight necessary?”) may not be appropriately scaled from a risk management perspective to ensure that the tactical use of aircraft is in clear alignment with a wildfire’s incident strategy and with broader agency and interagency fire management goals and objectives. To improve strategic risk management of aviation assets in wildfire suppression, we present a framework demonstrating a risk-informed strategic aviation decision support system, the Aviation Use Summary (AUS). This tool utilizes aircraft event tracking data, existing geospatial datasets, and emerging analytics to summarize incident-scale aircraft use and guide decision makers through a strategic risk management process. This information has the potential to enrich the decision space of the decision maker and supports programmatic transparency, enhanced learning, and a broader level of accountability.

The sustainability of bridge infrastructure is becoming increasingly important due to rising environmental, economic, and social demands. However, most current assessment models remain fragmented, often overlooking the social pillar, underutilizing risk integration across the lifecycle, and failing to fully leverage digital tools such as Building Information Modeling (BIM) and Life Cycle Sustainability Assessment (LCSA), resulting in incomplete sustainability evaluations. This study addresses these limitations by introducing a practical and adaptable model that integrates BIM, LCSA, and expert-driven risk prioritization. Five Hungarian bridge projects were modeled using Tekla Structures and analyzed in OpenLCA to quantify environmental, economic, and social performance. A custom Sustainability Level Change (SLC) algorithm was developed to compare baseline scenarios (equal weighting) with risk-informed alternatives, simulating the impact of targeted improvements. The results demonstrated that prioritizing high-risk sustainability indicators leads to measurable lifecycle gains, typically achieving SLC improvements between +2% and +6%. In critical cases, targeted enhancement scenarios, applying 5% and 10% improvements to top-ranked, high-risk indicators, pushed gains up to +12%. Even underperforming bridges exhibited performance enhancements when targeted actions were applied. The proposed framework is robust, standards-aligned, and methodologically adaptable to various bridge types and lifecycle phases through its data-driven architecture. It empowers infrastructure stakeholders to make more informed, risk-aware, and data-driven sustainability decisions, advancing best practices in bridge planning and evaluation. Compared to earlier tools that overlook risk dynamics and offer limited lifecycle coverage, this framework provides a more comprehensive, actionable, and multi-dimensional approach.

While traditional risk importance measures in probabilistic risk assessment are effective for ranking safety-significant components, they often overlook critical aspects such as the timing of accident progression and consequences. Dynamic probabilistic risk assessment offers a framework to quantify such risk information, but standardized approaches for estimating risk importance measures remain underdeveloped. This study addresses this gap by: (1) reviewing traditional risk importance measures and their regulatory applications, highlighting their limitations, and introducing newly proposed risk-triplet-based risk importance measures, consisting of timing-based worth, frequency-based worth, and consequence-based worth; (2) conducting a case study of Level 2 dynamic probabilistic risk assessment using the Japan Atomic Energy Agency’s RAPID tool coupled with the severe accident code of MELCOR 2.2 to simulate a station blackout scenario in a boiling water reactor, generating probabilistically sampled sequences with quantified timing, frequency, and consequence of source term release; (3) demonstrating that the new risk importance measures provide differentiated insights into risk significance, enabling multidimensional prioritization of systems and mitigation strategies; for example, the timing-based worth quantifies the delay effect of mitigation systems, and the consequence-based worth evaluates consequence-mitigating potential. This study underscores the potential of dynamic probabilistic risk assessment and risk-triplet-based risk importance measures to support risk-informed and performance-based regulatory decision-making, particularly in contexts where the timing and severity of accident consequences are critical.

Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble to improve risk-aware DeFi valuation, combining Extremely Randomized Trees (ETs), Support Vector Regression (SVR), and Categorical Boosting (CAT) as heterogeneous base learners, with a K-Nearest Neighbors (KNNs) meta-learner integrating their forecasts. Using an expanding-window panel time-series cross-validation design, the framework achieves significantly higher predictive accuracy than individual models, benchmark ensembles, and econometric baselines, obtaining RMSE = 0.085, MAE = 0.065, and R2 = 0.97—representing a 25–36% reduction in valuation error. Wilcoxon tests confirm that these gains are statistically significant (p < 0.01). SHAP-based interpretability analysis identifies Gross Merchandise Volume (GMV) as the primary valuation determinant, followed by Total Value Locked (TVL) and key protocol design features such as Decentralized Exchange (DEX) classification, while revenue variables and inflation contribute secondary effects. The findings demonstrate how explainable ensemble learning can strengthen valuation accuracy, reduce information-driven uncertainty, and support risk-informed decision-making for investors, analysts, developers, and policymakers operating within rapidly evolving blockchain-based digital asset environments.