Transport accounts for about one-quarter of global energy-related carbon-dioxide (CO2) emissions. However, decarbonization policies only show incremental progress as most jurisdictions lack an integrated approach. Siloed thinking between the energy and transport sectors holds back the development of more effective policies for transport decarbonization, although previous work has only reported this anecdotally. We systematically identify diverging perspectives between energy and transport experts globally at all levels of government. Using thematic and frequency analysis of expert elicitation survey responses combined with machine learning, we find that (1) both sectors tend to have a narrow focus that neglects broader issues and has led to policy failures; (2) views differ on which energy sources are sustainable with somewhat less variation on how to prioritize decarbonization measures; (3) both sectors support increased communication and coordination for better outcomes and efficiency; and (4) most experts anticipate that transport decarbonization will be insufficient to achieve net zero by 2050, with views varying more based on affiliation type and region. These results provide a starting point for governments to bridge the divide between the sectors and to formulate adequate policies enabling transport decarbonization in line with global climate and sustainability goals.

ABSTRACT Fusion energy is increasingly recognized as a potential game‐changer in addressing the grand challenge of achieving deep decarbonization while ensuring long‐term energy security. Recognizing the uncertainty surrounding fusion energy's technological maturity, commercialization timelines, and cost trajectories, this study adopts an anticipatory foresight approach tailored to high‐uncertainty contexts. The research employs a mixed‐methods framework incorporating horizon scanning, expert elicitation, trend analysis, and exploratory scenario planning. These methods were selected to account for deep technological uncertainty (e.g., plasma containment breakthroughs, cost convergence, fuel supply chain development), as well as systemic uncertainties related to sociopolitical acceptance and infrastructure readiness. For the case of Saudi Arabia, three distinct scenarios—Optimistic, Moderate, and Conservative—are developed to reflect a spectrum of plausible futures. Under the Optimistic Scenario, fusion could supply 10%–15% of Saudi Arabia's electricity mix by 2045 (50–75 TWh annually). The Moderate Scenario forecasts a 5%–10% contribution by 2050 (25–50 TWh), while the Conservative case sees fusion reaching under 5% by 2060 (< 25 TWh). These projections are framed within the broader uncertainty landscape, with sensitivity analyses on cost assumptions, technological learning curves, and policy interventions. A comparative assessment of anticipatory methodologies under these uncertainty levels underscores the limitations of deterministic forecasting and the value of scenario‐based planning in guiding long‐term energy policy. While fusion's economic feasibility remains uncertain, potential cost parity with advanced nuclear fission and gas‐fired plants by mid‐century is plausible. The paper concludes with strategic policy recommendations to reduce uncertainty and accelerate fusion adoption: increasing national R&D funding, fostering international and public‐private collaborations, investing in adaptive grid infrastructure, and developing flexible regulatory frameworks.

Foodborne disease and its sequelae exert a significant cost on Australia through health care costs, lost productivity, and occasional fatal illness. While estimating the cost of illness for all foodborne pathogens or for specific pathogens has value in quantifying this disease burden, it is also informative to estimate costs by food commodity and to identify priority areas for improving food safety. We combined a cost of illness model for foodborne illness in Australia with an expert elicitation of the food commodities associated with illness for key pathogens. The total cost of the six modeled pathogens was 721 million (June 2023 AUD), with campylobacteriosis having the greatest overall cost (AUD 420 million). Across food categories, AUD 328 million was attributed to poultry, AUD 107 million to vegetables, while dairy, beef, and pork each had costs over AUD 55 million. Strong associations were found between Campylobacter and poultry (69% of campylobacteriosis cases attributed to poultry) and Yersinia and pork (54% of yersiniosis cases attributed to pork). This study highlights poultry as a key cause of foodborne illness in Australia, responsible for almost half of the total costs due to Campylobacter, non-typhoidal Salmonella, Yersinia enterocolitica, Listeria monocytogenes, and Shiga-toxin producing Escherichia coli.

This study applies the Analytic Hierarchy Process (AHP) to optimize maintenance project selection at Egbin Power Plant, Nigeria. It addresses the need for a systematic approach beyond reactive methods by integrating four criteria—Economic, Performance, Technical, and Criticality—each with defined sub-criteria. Expert elicitation was conducted with eight professionals (Maintenance Managers, Operations Superintendents, Planning Engineers, and HSE Officers), and their judgments were aggregated using the geometric mean method. Validation through sensitivity analysis (weight perturbations, Monte Carlo simulations, tornado diagrams) and cross-method comparison with TOPSIS confirmed the robustness of results. Hydrogen Plant Overhaul (HPO) emerged consistently as the top priority, demonstrating novelty in contextual adaptation to Nigerian thermal power plant maintenance planning.

Abstract The 1650 CE Kolumbo (Greece) submarine eruption resulted in the reported deaths of 50 people and thousands of animals on Santorini (Thera) due to exposure to a cloud of noxious volcanic gases. Lack of ash in the cloud indicates that the gas release was unrelated to a magmatic explosive eruption. Medical evidence from historic accounts suggests lethal exposure to CO 2 and H 2 S, which were likely the main hazardous gases, whereas strongly acidic gases such as SO 2 , HCl, and HF were less relevant due to their high solubility in seawater. Expert elicitation indicates significant uncertainty, with probabilities of gas releases conditional on the occurrence of an eruption in the next 30 years ranging in 15–58–93% (5th–50th–95th percentiles), and a 2–17–48% likelihood of the gas cloud reaching Thera. A transient 3D multiphase fluid dynamics model (ASHEE) is employed to simulate turbulent gas cloud propagation and dilution under scenarios with source and meteorological conditions informed by expert elicitation and ECMWF-ERA5 2005−2016 data. A novel component of this study is the derivation of analytical predictive relationships through a non-dimensional scaling analysis, based on parameters like the Richardson number. Simple models are presented for weak, intermediate, and strong wind regimes, providing a generalized framework for gas transport and hazard assessment in similar volcanic settings. Results indicate about 50% probability of the gas cloud reaching Thera, even with a relatively modest volumetric flow rate of 10 3 m 3 s −1 and a wind speed half of the average. However, hazardous concentrations (above 200 ppm of H 2 S and 10 vol.% of CO 2 ) along the NE coast of Thera are reached if source gas flux exceeds 10 4 m 3 s −1 . By integrating elicitation outcomes, physical modeling, and probabilistic analysis, this study estimates a 16% and 17% likelihood of hazardous gas exposure for CO 2 and H 2 S, respectively, along the NE coast of Thera in case of formation of a gas density current in the next 30 years.

An expert elicitation to inform coastal management decision-making for mitigating future hazards.

An enhanced fuzzy cognitive map for human risk assessment in maritime transportation: Integrating causal mining and expert elicitation

IntroductionRisk assessment (RA) frameworks are increasingly being applied to improve the welfare of farmed animals. These frameworks have at their core, a logic chain linking welfare hazards (risks) with one or more welfare consequences which, in turn, are each measured by one or more welfare indicators. Effective and efficient monitoring of animal welfare often involves the selection of a subset of indicators from a large pool. Selecting ‘iceberg indicators’ could be advantageous due to their association with multiple welfare consequences. However, no standardised, data-driven method exists to select optimal combinations under practical constraints. This study addresses this gap by creating an algorithmic approach to optimise indicator selection.MethodsThe work was conducted in six phases: (1) construction of a structured database of welfare indicators; (2) a proof-of-concept study; (3) design of a greedy selection algorithm; (4) enhancement of the algorithm using branch-and-bound and backtracking methods; (5) performance and sensitivity testing, and (6) creation of two case studies. A dataset of 382 animal welfare indicators across seven farm species was compiled from scientific opinions published by the European Food Safety Authority (EFSA) and from other published literature. The EFSA scientific opinions contain data acquired through a rigorous process of literature reviews and expert elicitation and consensus panels to link welfare indicators with their associated welfare hazards and welfare consequences. To enable algorithm development, the Coverage of each welfare indicator was first determined by calculating the number of unique welfare consequences to which it was linked. Metadata such as the Impact of welfare consequence [Low (1) or High (2)], Ease of hazard mitigation [Easy (1), Moderate (2) or Difficult (3)], and Ease of indicator use [Easy (1), Moderate (2) or Difficult (3)] was generated through an expert elicitation process. These data were standardised using max–min normalisation across all criteria, and an objective function was defined which enabled indicator subset selection according to various user-defined criteria. Optimisation was performed using both a greedy algorithm and an enhanced algorithm incorporating backtracking and branch-and-bound solvers. Algorithm performance and robustness were evaluated through sensitivity analyses, scenario testing, and computational benchmarking.ResultsThe greedy algorithm offered computational efficiency but incorporated suboptimal plateaus in Coverage as additional indicators were combined. The enhanced algorithm identified globally optimal combinations within 0.2 s for all species, regardless of problem size. In a broiler chicken case study, the enhanced algorithm removed indicators that were moderately difficult to use. A pig case study showed that the enhanced algorithm combined the same welfare indicators as the greedy algorithm but validated the added value of multi-criteria scoring by identifying high-impact, easy-to-implement indicators suitable for welfare certification.DiscussionThe enhanced algorithm was able to move beyond the selection of iceberg indicators, by incorporating multiple selection criteria to inform welfare indicator choice. The enhanced algorithm is data-agnostic and enables users to optimise indicator selection with diverse datasets spanning research, industry, and policy contexts. Its flexibility supports the development of tailored applications for different stakeholders. Future work should explore processes to determine weighting values, scenario testing, robustness, and stakeholder engagement to maximise both relevance and practicality.

The International Union for Conservation of Nature (IUCN) Green Status of Species (GSS), introduced in 2021, is a global standard of measurement used to assess the level to which a species has recovered (i.e., is viable and providing its ecological function across its entire range). It is also used to evaluate how a species has responded to past conservation actions and the expected conservation gains and recovery potential it would receive in the short- and long-term future. Preliminary application of the GSS method has relied on expert knowledge from individuals or small groups of specialists. However, more accurate and reliable results are likely to be produced by formally eliciting individual judgments from a diverse range of experts, followed by discussion, reevaluation, and synthesis of these judgments. We developed a method in which 2 structured expert elicitation workshops are used to conduct GSS assessments and applied this method to 8 Australian freshwater fish species from the Murray-Darling Basin. We integrated the investigate, discuss, evaluate, aggregate protocol into the GSS methods; experts assessed the species' IUCN Green Score (percent recovery) in the current state and for 5 other scenarios in the past and future with and without conservation. Four GSS conservation impact metrics were calculated based on the averages of expert judgments. Experts forecasted that impact in the short-term would be minimal (i.e., conservation gain metric=zero or low) for 5 of the 8 species because targeted and maintained conservation actions are often lacking. In contrast, experts indicated long-term recovery potential would be considerably higher if implementation of appropriate recovery activities could be sustained (all 8 species had medium or high recovery potential). We concluded that the GSS is well suited to a modified workshop approach because it aims to reduce biases associated with expert judgments and encourages valuable knowledge sharing among experts.

Abstract California has ambitious offshore wind goals of 2–5 GW by 2030 and 25 GW by 2045. The coastline deep ocean floor calls for floating offshore wind (FOSW), a new technology whose application has yet to be built to scale. Given the novelty, deep uncertainty, and lack of data regarding FOSW, we fielded an expert elicitation regarding the costs, probability and duration of failure, and likely potential system architectures. We find that there is significant disagreement among experts: cost estimates vary by a factor of at least 3. Probabilities of failure range from 0.01% to 20% for most parts of the system. Experts diverged on likely transmission configurations that are likely to be used with FOSW projects, though most agreed DC technologies will be used in the future. Overall, experts believe California’s 2030 FOSW targets will not be met but could be achieved by 2035, and 2045 targets could be realized with faster buildout of future lease areas.

Abstract. Continued anthropogenic pressures on the Earth system hold the potential to disrupt established circulation patterns in the ocean and atmosphere. In this narrative review, we investigate tipping points in these systems by assessing scientific evidence for feedbacks that may drive self-sustained change beyond critical forcing thresholds, drawing on insights from expert elicitation. The literature provides multiple strands of evidence for oceanic tipping points in the Atlantic Meridional Overturning Circulation (AMOC), the North Atlantic subpolar gyre (SPG), and the Antarctic Overturning Circulation, which may collapse under warmer and “fresher” (i.e. less salty) conditions. A slowdown or collapse of these oceanic circulations would have far-reaching consequences for the rest of the climate system and could lead to strong impacts on human societies and the biosphere. Among the atmospheric circulation systems considered, a few lines of evidence suggest the West African monsoon (WAM) as a tipping system. Its abrupt changes in the past have led to vastly different vegetation states of the Sahara (e.g. “green Sahara” states). Despite multiple potential sources of destabilization, evidence about tipping of the monsoon systems over South America and Asia is limited. Although theoretically possible, there is currently little indication for tipping points in tropical clouds or mid-latitude atmospheric circulations. Similarly, tipping towards a more extreme or persistent state of the El Niño–Southern Oscillation (ENSO) is currently not fully supported by models and observations. While the tipping thresholds for many of these systems are uncertain, tipping could have severe socio-environmental consequences. Stabilizing Earth's climate (along with minimizing other environmental pressures, such as aerosol pollution and ecosystem degradation) is critical for reducing the likelihood of reaching tipping points in the ocean–atmosphere system.

Cost overruns in road construction projects significantly challenge project efficiency and financial sustainability. This study develops an integrated Bayesian-Monte Carlo framework to identify and quantify critical cost overrun factors in Pakistan's road infrastructure projects. Through literature review and expert consultation, 25 potential causes were systematically reduced to 10 critical factors using expert elicitation and criticality scoring. Bayesian analysis calculated posterior probabilities by combining expert judgment with conditional relationships, while Monte Carlo simulation quantified uncertainties and provided probabilistic ranges for each factor. The framework reveals design error changes as the most critical factor (mean: 61.5%, maximum: 93.3%), followed by variation orders (58.9%) and inaccurate estimates (57.8%). Secondary contributors include land acquisition issues (54.5%) and schedule delays (52.3%). This integrated approach enables evidence-based risk prioritization, replacing arbitrary contingency planning with data-driven decision making. The methodology provides project managers with actionable insights for targeted risk mitigation and optimal resource allocation in resource-constrained environments.

A vast amount of expert knowledge currently remains inaccessible to digital information systems. Expert knowledge elicitation is a systematic approach to accessing and synthesizing the insights of subject matter experts, especially when available objective data are incomplete. In plant pathology, expert knowledge elicitation is valuable for addressing urgent, uncertain, and/or future challenges, such as emerging disease threats, complex epidemiological systems, knowledge gaps when resources are limited, and future scenarios. This perspective explores when expert knowledge elicitation is most effective for addressing plant health challenges, emphasizing its role in informing timely, expert-based decisions. We discuss lessons learned from real-world implementations across diverse regions and pathosystems, highlighting strategies for eliciting, structuring, and interpreting expert-derived data, as well as associated caveats. We frame expert knowledge as a form of big data and outline how existing big-data streams (e.g., remote sensing, crowdsourced reports, and digital surveillance) can inform expert judgements. Outputs from expert knowledge elicitation can be captured as scalable datasets (text, tabular, audio, and video) that enable artificial intelligence-supported synthesis. We illustrate how expert knowledge can be integrated in Bayesian analyses, providing a transparent and rigorous approach to understanding uncertainty and improving inference. Finally, we outline future opportunities, including integration with artificial intelligence, to scale and strengthen expert knowledge elicitation in support of global plant health. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

The final step of the burden of foodborne disease estimation is to attribute the estimated burden to foodborne transmission, and to specific foods. A variety of source attribution approaches and methods are available, including approaches that apply data from public health surveillance, food consumption, and food monitoring programs, epidemiological approaches, expert elicitations, and intervention studies. After reviewing available source attribution methods and their applicability to estimate the proportion of disease burden by foodborne hazards attributable to transmission pathways and to specific foods considering data requirements and availability at regional and country level, FERG1 concluded that expert elicitation is the preferred method for source attribution in a context of data heterogeneity across countries and pathogens. For the 2021-2025 iteration, the FERG recommended to the WHO that a new Structured Expert Elicitation is conducted for source attribution of 40 hazards. The WHO launched a call for experts in July 2023, and further identified experts in a snowball process. The update of the expert elicitation study will be able to capture recent changes in food consumption, food availability, economic conditions, and sanitation conditions in specific parts of the world. Improvements of methods leveraged on lessons learned and on technological developments for interviewing experts and data analysis. This presentation will describe the rational, methodology, and preliminary results of the source attribution study, and discuss estimates in light of underlying uncertainties and other sources of evidence.

Abstract Species that are secretive, imperilled and consequently data deficient often require conservation action despite limited available information. In such scenarios, Bayesian networks (BNs) offer a versatile and intuitive approach for utilizing various information sources, including literature reviews, community science datasets and expert knowledge. Although it has been suggested that peer review be incorporated during expert elicitations in a BN modelling context, little information exists about how to implement this approach or about how models constructed using this approach perform. We documented a double‐blind peer review approach for expert elicitation in a BN modelling context. Further, we compared BN models that were generated by experts who engaged in this peer‐review process (PRBNs) to those that were generated by a single expert whose knowledge was supplemented only by a literature review (LRBNs). These comparisons were based on the ability to predict the occurrence (via community science and satellite telemetry data) of a secretive and data deficient species, the King Rail ( Rallus elegans ), throughout a large region. We found that the LRBNs tended to predict King Rail occurrence as well as, or better than, the PRBNs. The LRBNs that we evaluated provided more consistent predictions across our study area. However, preliminary data suggest that the PRBNs may better distinguish between locations of focal and non‐focal species within smaller regions. Practical implication . Our framework for utilizing double‐blind peer review could serve as a useful guide and have practical implications for incorporating expert knowledge in BN models. Further, our model comparison case study suggests that, in some contexts, a single expert who uses a literature review to inform the creation of BN models may be able to accurately predict the occurrence of a secretive and data‐deficient focal species. Taken together, this information could help ecologists decide when a double‐blind peer review approach to expert elicitation is necessary and how to implement this approach in a BN modelling context.

Abstract Small‐scale fisheries support millions of people globally, but if poorly monitored and managed, they can negatively impact threatened marine species like sharks. We explore approaches to assess the ecological sustainability of an extremely data‐limited, small‐scale fishery for blacktip sharks (Carcharhinus limbatus) in Goa, India. We use an adapted expert elicitation approach, modified to suit local fishing communities, to collect data on shark catch and develop exploratory population models to understand conditions under which the fishery could be sustainable. An estimated 13,881–15,616 newborn blacktip sharks are targeted and captured annually by gillnets across our study sites. Our adapted expert elicitation protocol can serve as a rapid, cost‐effective, and inclusive method to obtain critical data for conservation planning, especially in data‐limited, Global South contexts. Our population models reveal that the current levels of shark harvesting are unlikely to be sustainable and can only continue if harvest rates are reduced by at least half and if the current local shark population is relatively high. Our study provides crucial information to inform conservation decision‐making, highlighting the need for urgent intervention to regulate Goa's shark fishery. Working with the local community and understanding the socio‐economic dimensions of this fishery can help identify appropriate conservation interventions.

Climate change is increasingly affecting the aquaculture sector, particularly in estuarine systems that support high-value production. In the Galician Rías Baixas, where shellfish farming is a cornerstone of the coastal economy, rising sea temperatures, sea-level rise, and changing precipitation patterns pose significant risks to mussel aquaculture. This study presents a spatially explicit Aquaculture Suitability Similarity Index (ASI) designed to identify alternative cultivation areas that replicate the environmental and logistical characteristics of historically successful mussel farms. The ASI integrates a set of environmental variables (water temperature, salinity, biogeochemical quality, current velocity, and wave height) and technical constraints (depth and distance to port), with factor weights derived via expert elicitation using the Delphi method. Results show that most waters are highly similar to current farming areas, suggesting strong potential for spatial expansion or relocation. In contrast, areas near the mouths of the rías and the adjacent continental shelf show lower suitability due to greater oceanic exposure and associated logistical challenges. The ASI provides a robust, transferable tool to inform aquaculture spatial planning and climate adaptation strategies. Its methodological framework can be adapted to other regions and species, supporting evidence-based decision-making for sustainable aquaculture development.

ABSTRACTIn absence of sufficient data, structured expert judgment is a suitable method to estimate uncertain quantities. While such methods are well established for individual variables, eliciting their dependence in a structured manner is a less explored field of research. We tested the performance of experts in constructing and quantifying a nonparametric Bayesian network, describing the correlation between river tributary discharges. Specialized software was provided to assist the experts. Expert performance was investigated using the dependence calibration score (a correlation matrix distance metric) and the likelihood of the joint distribution. Desirable properties of the dependence calibration score were investigated theoretically. Individual expert judgments were combined based on performance into a group opinion aka decision maker. All experts were able to create and quantify a correlation matrix between 10 variables that resembled the correlations between observed discharges well. The decision makers performed similarly to the best expert. Based on the metrics investigated, it mattered little which expert opinions and with what weight were combined in a decision maker. This is partly because all experts performed well. Adding a bad performing expert increased the positive effect of performance‐based weighting, underscoring the importance of developing scoring rules for dependence elicitation. The overall results are promising: Aided by specialized graphical software, the experts in this study were able to quickly create and quantify dependence structures.

Background: A dearth of evidence exists on how to include children and young people in palliative care research. Aim: We aimed to identify successful practices in involvement, recruitment and data collection with children and young people with life-limiting illness in research. Design: We synthesised methods from five primary studies from three geographical regions in which children with life-limiting conditions were recruited and interviewed. Using Expert Elicitation Methodology we identified successful practices in the three areas of involvement, recruitment and data collection. We established consensus on methodological challenges and solutions, and developed 10 recommendations for inclusion in research protocols. Setting: Primary cross-national research in three regions; Middle East (one study), sub-Saharan Africa (one study), Europe (three studies), reporting on studies that recruited N = 244 children aged 5–18 years. Results: Recommendations are: (1) research team supported by advisory group of children for entire research process; (2) appropriate distress protocol tailored to population; (3) opt not to use term ‘palliative care’ in study materials if significant distress is a risk; (4) be deliberate in purposive sampling to ensure diagnoses heterogeneity where appropriate; (5) age-appropriate information materials pre-tested by children; (6) clinical teams receive training in recruitment; (7) time to build rapport before starting data collection; (8) consider potential biases and advantages of having parent/carer present during interview; (9) use age-appropriate toys/games during interviews; (10) selfcare for researchers to manage distress. Conclusions: These recommendations can guide design and conduct of research, enabling children with life-limiting illness to meaningfully participate and express their views.

In the absence of long-term data, structured expert elicitation gathers expert judgments and associated uncertainties to assess the clinical plausibility of long-term extrapolations. The objective of this study was to obtain expert estimates of expected long-term outcomes for advanced renal cell carcinoma treatments to inform cost-effectiveness analysis for National Institute for Health and Care Excellence (NICE)'s pathways pilot. Using materials from the structured expert elicitation resources (STEER) repository, aligned with the Medical Research Council (MRC) protocol, the exercise was planned and conducted. Aiming for 5-10 oncologists from diverse UK geographies and settings, experts estimated progression-free survival (PFS) at three landmark timepoints for 21 disease-risk-prior treatment combinations and overall survival for best supportive care. Within an 8-week timeframe, we piloted with one clinician, conducted online training, collected responses via an online survey using the roulette method and mathematically aggregated results through linear opinion pooling. Nine experts participated (question response rate: 95%). For first-line intermediate/poor-risk patients, clinicians projected similar PFS for three immune oncology/tyrosinekinase inhibitor (TKI) combinations from 5years onward and comparable PFS for two TKI monotherapies. Nivolumab+ipilimumab was anticipated to achieve the highest PFS amongst first-line therapies. Expert reasoning incorporated treatment class, clinical experience, and awareness of trial data optimism. Expert estimates were generally somewhat optimistic compared with observed UK real-world evidence and pessimistic compared with observed trial data. Structured expert elicitation is a pragmatic, efficient approach for informing long-term survival extrapolations in the context of a rapidly evolving treatment pathway. We demonstrated that expert elicitation is possible even for complex decision problems in a relatively short timeframe.