Securing the availability of enough metals to fulfill demand is a critical societal concern. Models of metal supply systems can help enhance our understanding of these systems and identify strategies to reduce material criticality and improve resilience. In this work, we introduce a novel approach to modeling metal supply systems, using nickel as a case study. Our approach combines system dynamics modeling, in which various feedback loops influence future outcomes, with the higher sectoral and geographical detail of industrial ecology (IE) methods and data on individual mines. We also include extensive uncertainty analyses through exploratory modeling and analysis. Using this combined modeling approach, we explore the development and resilience of the global nickel supply system between 2015 and 2060 under various uncertainties and policy levers. Our results show that incorporating feedback effects leads to more realistic demand behavior and resource depletion patterns compared to traditional dynamic material flow analysis. Market feedback enhances resilience, but cannot fully offset criticality risks. Sectoral disaggregation reveals increased criticality risks due to the energy transition, which can be mitigated by increasing opportunities for substitution, product lifetime extension, recycling, exploration, capacity expansion, and by-product recovery. Geographical disaggregation highlights the resilience benefits of diverse supply sources, as well as the effects of changing regional market shares on sustainability impacts, ore grade variability, and by-product dynamics. Our combined modeling approach is a step toward prospective, dynamic criticality assessment, in which system changes and future risks are accounted for when determining material criticality and policy recommendations.

Sustainable management of water resources is crucial for humanity. However, traditional methods for achieving this are becoming obsolete. This is because they are underpinned by the assumption that we have a good understanding of how water availability and demand will change in the future. However, based on our current experience with climate change, this is not the case. In fact, rather than having a good understanding of what the future might look like, it is, in fact, deeply uncertain. Consequently, a new paradigm for water resources management is needed; one that accounts for deep uncertainty by embracing scenario thinking. We categorize and summarize different causes of deep uncertainty in water resources management and provide examples of how an emerging paradigm rooted in scenario thinking can deal with these. We hope to stimulate discussion to enable this new paradigm to be developed further and embedded in standard practice.

Geopolitical tensions and conflicts can disrupt energy markets, threatening international energy supply security and imposing financial stress on energy-intensive industries reliant on imported fossil fuels. Exploring the challenges and opportunities associated with supply diversification is crucial for understanding the potential for hard-to-abate industry decarbonization under the risk of future energy price shocks. In this context, we investigate the role of green hydrogen as a viable and sustainable alternative to natural gas applications in iron and steel manufacturing. We first quantify how the integration of green hydrogen into the existing infrastructure can complement stringent climate action ambitions in reducing CO2 emissions over the next five decades. We find that green hydrogen acts as a transitional technology, enabling a gradual shift towards electrification of heat supply while bridging the gap until low-carbon steel technologies become commercially feasible. Furthermore, we assess the benefits of timely green hydrogen investments in mitigating the economic repercussions of unforeseen natural gas price surges. Overall, this study underscores the potential of green hydrogen in decarbonizing the iron and steel industry while promoting energy independence, but it also highlights its contingency on sufficiently ambitious climate policies and adequate technological advancements.

The police control room determines where to send available police units to intercept a fleeing fugitive. Models can support the police with decision-making for fugitive interception. The police have, at most, a few minutes to determine an interception strategy. Therefore, a timely calculation of the interception positions is essential to support police interception operations. The number of nodes in the network, each being a crossing where routes of the fleeing suspect can split, greatly contributes to the computation time. Graph coarsening is a promising approach to reduce the complexity of the network, and therefore the computation time. We compare four graph coarsening algorithms on five road networks and assess their impact on computation time and solution quality for the fugitive interception problem. Based on the comparison, we propose and test a new method specifically for fugitive interception. This method, Search Space Representation, improves the quality of the best solutions obtained by the optimization algorithm with up to 12%, improves the reliability of the optimization to find high-quality solutions, and decreases the number of function evaluations required to obtain high-quality solutions to 5000–10,000 depending on the size and complexity of the road network, which is feasible for real-time decision-making. Search Space Representation can be applied to reduce the computation time of other network-based optimization problems.

Humanitarian and military organizations face deeply uncertain, continuously changing environments due to disasters and conflict. Information sharing is vital to adapt to these disruptions effectively and ensure the timely availability of essential equipment and supplies anywhere in the world. However, little is known about the role of information sharing in adapting to a changing environment. We use an agent-based discrete-event simulation to study information-sharing mechanisms, specifically delayed information-sharing behavior, and analyze how they impact the adaptation of decision-making structures over time. Drawing upon adaptation models from literature, we develop a model in which agents share information and endogenously create these structures. We experiment with various levels of information delays in dynamically changing environments and assess how this affects adaptation and performance. Our findings unveil that horizontal delays lead to earlier hierarchical expansion and vertical delays slow decision-making in crisis response environments.

The robust decision-making framework (RDM) has been extended to consider multiple objective functions and scenarios. However, the practical applications of these extensions are mostly limited to academic case studies. The main reasons are: (i) substantial cognitive load in tracking all the trade-offs across scenarios and the interplay between uncertainties and trade-offs, (ii) lack of decision-makers’ involvement in solution generation and confidence. To address these problems, this study proposes a novel interactive framework involving decision-makers in searching for the most preferred robust solutions utilizing interactive multiobjective optimization methods. The proposed interactive framework provides a learning phase for decision-makers to discover the problem characteristics, the feasibility of their preferences, and how uncertainty may affect the outcomes of a decision. This involvement and learning allow them to control and direct the multiobjective search during the solution generation process, boosting their confidence and assurance in implementing the identified robust solutions in practice.

Adaptive pathways planning is an approach that maps the solution space over time to inform decision making under uncertainty. Since its first applications to climate change adaptation in the ’10s several studies and practical applications have used and extended the approach and discussed its benefits, limits, and complexity. What have we learned from a decade of adaptive pathways studies? This paper elaborates lessons learned on the use, value and weaknesses of adaptive pathways approaches for decision making using a set of guiding questions related to the decision context, the methods used, and contributions to decision making. Based on our experience and literature review, we find that: a) adaptive pathways analyses have been applied widely and are moving from theory to practice; b) an adaptive pathways analysis can be tailored and typically follows a staged approach; c) methods include narratives, impact models, and stakeholder participation tools; d) the complexity of adaptive pathways as a result of multiple actors, values, hazards, and actions at various scales for different purposes is a challenge, and this is increasingly considered through various extensions and combinations with other approaches. Ways forward to address weaknesses and current challenges include: accounting for coevolution between multiple actors across different scales (e.g., through interactive and multilevel pathways) and combining an adaptive pathways analysis with visioning and backcasting approaches for transformative adaptation and operationalizing climate resilient development pathways. To enable further applications in practice, it is important that experiences are shared and governance issues (e.g. long-term planning and funding) addressed.

Many European cities are investigating how to transition to climate-neutral transport systems. Due to the transport system's complexity and uncertainty about the future, identifying drivers and choosing effective policies to make the city more sustainable is challenging. Additionally, the chosen policies need to be supported by relevant actors. This study aims to support the municipality of The Hague in generating robust policies supported by and within the municipality. We build on participatory modeling and decision-making under deep uncertainty to create a novel approach to address this goal. In two workshops, the participants formulated goals and objectives, created Causal Loop diagrams, and identified potential interventions. Using a set of possible futures, the interventions were then stress-tested to evaluate their robustness. By explicitly linking, for the first time, participatory modeling and decision-making under deep uncertainty approaches, the participants could understand the system better and deal with uncertainty. Participants gained insight into systemic complexity and methods to deal with it, the inter-relatedness of interventions and their effects, and a shared understanding of the problem and its scope. This study demonstrates the potential of a novel approach to generate supported robust interventions to achieve the goal of a climate-neutral transport system.

Supply chain visibility concerns the ability to track parts, components, or products in transit from supplier to customer. The data that organizations can obtain to establish or improve supply chain visibility is often sparse. This paper presents a classification of the dimensions of data sparseness and quantitatively explores the impact of these dimensions on supply chain visibility. Based on a review of supply chain visibility and data quality literature, this study proposes to characterize data sparseness as a lack of data quality across the entire supply chain, where data sparseness can be classified into three dimensions: noise, bias, and missing values. The quantitative analysis relies on a stylized simulation model of a moderately complex illicit supply chain. Scenarios are used to evaluate the combined effect of the individual dimensions from actors with different perspectives in the supply chain, either supply or demand-oriented. Results show that when a data sparseness of 90% is applied, supply chain visibility reduces to 52% for noise, to 65% for bias, and to 32% for missing values. The scenarios also show that companies with a supply-oriented view typically have a higher supply chain visibility than those with a demand-oriented view. The classification and assessment offer valuable insights for improving data quality and for enhancing supply chain visibility.

Tin is an important metal for society with a high risk of supply disruptions. It is, therefore, classified as a critical material in many parts of the world. An exception is the European Union, for which tin was classified as a non-critical material in 2023. However, there are many discrepancies in the literature regarding the definitions and values of the indicators used to determine tin criticality in general, and recycling indicators in particular. Values for end-of-life recycling rate (EoL RR) range between 20% and 75%, and values for end-of-life recycling input rate (EoL RIR) range between 11% and 32%. In this paper, we critically assess the circularity and criticality indicator values for tin and calculate new values using material flow analysis. The new values for tin recycling indicators are lower than those used in most previous research, with a global EoL RR of 16% and an EoL RIR of 11% in 2017. Based on the updated recycling values, combined with a highly concentrated supply, high import reliance, and difficult substitution, we argue that the European Union should classify tin as a critical material. This reclassification can lead to more policy attention for tin, which can help reduce the impact of future supply disruptions and increase the resilience of the European and global tin supply chains.

Illicit supply chains for products like counterfeit Personal Protective Equipment (PPE) are characterized by sparse data and great uncertainty about the operational and logistical structure, making criminal activities largely invisible to law enforcement and challenging to intervene in. Simulation is a way to get insight into the behavior of complex systems, using calibration to tune model parameters to match its real-world counterpart. Calibration methods for simulation models of illicit supply chains should work with sparse data, while also tuning the structure of the simulation model. Thus, this study addresses the question: “To what extent can various model calibration techniques reconstruct the underlying structure of an illicit supply chain when varying the degree of data sparseness?” We evaluate the quality-of-fit of a reference technique, Powell's Method, and three model calibration techniques that have shown promise for sparse data: Approximate Bayesian Computing, Bayesian Optimization, and Genetic Algorithms. For this, we use a simulation model of a stylized counterfeit PPE supply chain as ground truth. We extract data from this ground truth and systematically vary its sparseness. We parameterize structural uncertainty using System Entity Structure. The results demonstrate that Bayesian Optimization and Genetic Algorithms are suitable for reconstructing the underlying structure of an illicit supply chain for a varying degree of data sparseness. Both techniques identify a diverse set of optimal solutions that fit with the sparse data. For a comprehensive understanding of illicit supply chain structures, we propose to combine the results of the two techniques. Future research should focus on developing a combined algorithm and incorporating solution diversity.

Schedule design in the transportation and logistics sector is a widely studied problem. Transport service providers, such as the train industry and aviation, aim for schedules to be on-time according to the planning (i.e., on-time performance or OTP) in order to increase the service level by ensuring that passengers actually make their connections and to reduce costs. Transportation services also aim for schedules that serve a high variety of destinations and frequency of connections (i.e., connectivity). OTP and connectivity are both highly dependent on buffer time: more lucrative connections can often be offered by reducing the buffer time in the schedule, while more delay can be absorbed by more buffer time. Given strict constraints on the minimum turnaround time of aircraft and minimum (and maximum acceptable) transfer times of passengers, assigning buffer time in an already tightly planned schedule to optimize OTP and connectivity simultaneously is a big challenge. This research presents a novel multi-objective formulation of a daily flight schedule where buffer scheduling is used to ensure the optimal balance between OTP of the schedule and the passenger connections as connectivity, given the tight restrictions. This problem formulation is solved using a simulation–optimization framework. Specifically, we use the Multi-Objective Evolutionary Algorithm (MOEA) BORG. As a proof of concept, a daily European flight schedule of a large international airline is optimized on both OTP and connectivity. The results demonstrate that the presented multi-objective formulation and associated solving through simulation–optimization can result in candidate schedules with both better on-time performance and a higher connectivity.

Evolutionary Multi-Objective Direct Policy Search (EMODPS) is a prominent framework for designing control policies in multi-purpose environmental systems, combining direct policy search with multi-objective evolutionary algorithms (MOEAs) to identify Pareto approximate control policies. While EMODPS is effective, the choice of functions within its global approximator networks remains underexplored, despite their potential to significantly influence both solution quality and MOEA performance. This study conducts a rigorous assessment of a suite of Radial Basis Functions (RBFs) as candidates for these networks. We critically evaluate their ability to map system states to control actions, and assess their influence on Pareto efficient control policies. We apply this analysis to two contrasting case studies: the Conowingo Reservoir System, which balances competing water demands including hydropower, environmental flows, urban supply, power plant cooling, and recreation; and The Shallow Lake Problem, where a city navigates the trade-off between environmental and economic objectives when releasing anthropogenic phosphorus. Our findings reveal that the choice of RBF functions substantially impacts model outcomes. In complex scenarios like multi-objective reservoir control, this choice is critical, while in simpler contexts, such as the Shallow Lake Problem, the influence is less pronounced, though distinctive differences emerge in the characteristics of the prescribed control strategies.

Infrastructure in low-lying coastal areas faces challenges from climate change, sea level rise, and the impact of compound hazards. Dynamic adaptive pathways planning (DAPP) is increasingly being applied as a way of planning under deep uncertainty. Stress testing for robustness is an integral part of DAPP which provides decision-makers with confidence. We outline a seven-step approach—combining scoping workshops, systems mapping, DAPP, exploratory modelling, robust decision-making, real options analysis and validation workshops—to support decision-making for infrastructure in low-lying coastal areas. We apply the seven steps to two wastewater treatment plant (WWTP) case studies in New Zealand to quantify indicators, signals, triggers and adaptation thresholds within DAPP plans and to identify adaptation pathways that are robust against future uncertainty. Case study one focuses on the implementation of an existing DAPP at Helensville WWTP. Our modelling enabled the challenge of quantifying indicators for adaptation thresholds and triggers to be overcome. We show that an adaptation threshold occurs at 31 cm of RSLR, the trigger point is sufficient lead time to enable relocation, and the indicator is the rate of observed RSLR. Case study one demonstrates in a quantitative way how an existing DAPP can be functionally implemented by a water management agency. Modelling for case study two, the Seaview WWTP, showed that 26 cm and 56 cm of RSLR are key thresholds. Nuisance flooding may occur after 26 cm of RSLR, which could happen as early as 2040 under a high emissions scenario. Inundation of plant assets may occur after 56 cm of RSLR, which could occur as early as 2060. Modelling showed that implementing changes to plant layout would allow the plant to remain on site for its design life (until 2080). Five adaptation archetypes were developed—sequences of adaptive actions that achieve the performance objective of continuing levels of service and avoid inundation of WWTPs. The seven-step approach is a way to stress-test a DAPP, to quantify signals, triggers and adaptation thresholds and to simulate implementation of a DAPP under a range of scenarios. This can facilitate more robust decision-making for wastewater infrastructure assets under future uncertainty.

Simulation–optimization models are well-suited for real-time decision-support to the control room for search and interception of fugitives by Police on a road network, due to their ability to encode complex behavior while still optimizing the interception. The typical simulation–optimization configuration is simulation model optimization, where the simulation model describes the system to be optimized, and the optimizer attempts to find the combination of decision variables that maximizes the interception probability. However, the repeated evaluation of the simulation model leads to high computation time, thus rendering it inadequate for time-constrained decision contexts. To support police interception operations in real-time, timely calculation of the solution is essential. Sequential simulation–optimization, where the simulation model, with its rich behavior, constructs (part of) the constraints of an optimization problem, could decrease the computation time. We compare the computation time for two configurations of simulation–optimization (typical simulation model optimization and sequential simulation–optimization) for various problem instances of the fugitive interception problem. We show that sequential simulation–optimization reduces the computation time of large instances of the fugitive interception case study ten-fold. This result illustrates the potential of sequential simulation–optimization to mitigate the expensive optimization of simulation models.

Mesopelagic fishes are a vital component of the biological carbon pump and are, to date, largely unexploited. In recent years, there has been an increased interest in harvesting the mesopelagic zone to produce fish feed for aquaculture. However, great uncertainties exist in how the mesopelagic zone interacts with the climate and food webs, presenting a dilemma for policy. Here, we investigate the consequences of potential policies relating to mesopelagic harvest quotas with a dynamic social-ecological modeling approach, combining system dynamics and global sensitivity analyses informed by participatory modeling. Our analyses reveal that, in simulations of mesopelagic fishing scenarios, uncertainties about mesopelagic fish population dynamics have the most pronounced influence on potential outcomes. The analysis also shows that prioritizing the development of the fishing industry over environmental protection would lead to a significantly higher social cost of climate change to society. Given the large uncertainties and the potential large impacts on oceanic carbon sequestration, a precautionary approach to developing mesopelagic fisheries is warranted.

This paper explores the spatial evolution of a flood-prone, sub-urban coastal area, Municipio X of Rome. The study investigates land use change through a diachronic analysis, providing empirical data to retrace the implication of the factors that shaped the study area and highlighting the connection between environmental vulnerabilities and planning measures. Qualitative and quantitative assessments are provided to understand the political and social factors that contributed to rapid urbanization in the area. This investigation aims to grasp how past developments influence current issues and assist planners and decision-makers in tackling present and future vulnerabilities more effectively.

Decision-making under uncertainty is important for managing human-natural systems in a changing world. A major source of uncertainty is linked to the multi-actor settings of decisions with poorly understood values, complex relationships, and conflicting management approaches. Despite general agreement across disciplines on co-producing knowledge for viable and inclusive outcomes in a multi-actor context, there is still limited conceptual clarity and no systematic understanding on what co-production means in decision-making under uncertainty and how it can be approached. Here, we use content analysis and clustering to systematically analyse 50 decision-making cases with multiple time and spatial scales across 26 countries and in 9 different sectors in the last decade to serve two aims. The first is to synthesise the key recurring strategies that underpin high quality decision co-production across many cases of diverse features. The second is to identify important deficits and opportunities to leverage existing strategies towards flourishing co-production in support of decision-making. We find that four general strategies emerge centred around: promoting innovation for robust and equitable decisions; broadening the span of co-production across interacting systems; fostering social learning and inclusive participation; and improving pathways to impact. Additionally, five key areas that should be addressed to improve decision co-production are identified in relation to: participation diversity; collaborative action; power relationships; governance inclusivity; and transformative change. Characterising the emergent strategies and their key areas for improvement can help guide future works towards more pluralistic and integrated science and practice.

Cities play a critical role in developing adaptable strategies to address the challenges posed by climate change. However, the inherent complexity of urban environments and their uncertain future conditions necessitate exploring innovative approaches and tools to assist the current planning practices. This paper presents a workflow rooted in model-based scenario planning for long-term adaptation planning given uncertain futures. To demonstrate the workflow's effectiveness, a pertinent case study was conducted in a flood-prone area of Rome. The study employed a land-use change model to examine potential urban growth patterns, considering the uncertain implementation of new poles of attraction. This interdisciplinary study constitutes an initial stride toward implementing uncertainty within urban planning frameworks. Future prospects encompass the integration of multiple models for cross-scale analysis, embracing further critical environmental and social aspects. This research contributes to advancing urban resilience strategies. It enhances the understanding of adapting to an uncertain future in the face of climate change, as urban areas must embrace comprehensive planning to ensure flexible adaptation when faced with climate-driven uncertainties in long-term planning. In conclusion, the study underscores that embracing uncertainty is a challenge and a pivotal opportunity to shape resilient and adaptable urban futures.

Integrated Assessment Models (IAMs) vary widely in complexity and underlying assumptions. There have been considerable efforts to increase the complexity of IAMs for improved representation of socioeconomic and environmental outcomes. However, less attention has been given to the foundational assumptions of these models and their distributional consequences. These assumptions are fraught with deep and normative uncertainty and can significantly impact IAM projections. If these assumptions are not explicit, IAMs can perpetuate existing mistakes and exacerbate inequalities due to their black-box nature. This paper introduces a novel IAM called JUSTICE (Justice Universality Spatial Temporal Integrated Climate Economy) to explore the influence on distributive justice outcomes due to underlying modelling assumptions across model components and functions: the economy and climate components, and the damage and social welfare functions. JUSTICE is a simple IAM inspired by the long-established RICE and is designed to be a surrogate for more complex IAMs for eliciting normative insights. As illustrated in Figure 1, JUSTICE contains two distinct economic and climate sub-models, three damage functions, and four social welfare functions (SWFs), each based on fundamentally different assumptions. This modular structure enables JUSTICE to uncover assumptions with nontrivial normative and distributional consequences. Also, the simplicity of JUSTICE makes it suitable for assessing the consequences of these modelling assumptions under deep and normative uncertainty using MS-MORDM and EMODPS frameworks, promoting a more equitable approach to decision-making. Using JUSTICE, we investigate the effects of three SWFs—Utilitarianism, Egalitarianism, and Prioritarianism—on global temperature rise, with two levels of aggregation. We also explore the sensitivity of distributional outcomes for two different climate models. Our findings reveal that different assumptions lead to significantly distinct optimal abatement pathways, underscoring the importance of explicating assumptions and exploring their uncertainties to facilitate deliberation and identify common ground among policymakers with diverse perspectives.