Risk Scenarios Research Articles

A Frailty Model for Semi-competing Risk Data with Applications to Colon Cancer

In semi-competing risks (which generalizes the competing risks scenario), a subject may experience both terminal and non-terminal events, usually dependent, where the event time to the intermediate non-terminal event (say, tumor recurrence in cancer studies) is subject to censoring by the terminal event (say, death), but not vice-versa. As an alternative to the latent failure time formulation of semi-competing risks with joint survival functions, here, we consider an illness-death (multistate) shared frailty framework, where the dependency between the terminal and non-terminal failure times is incorporated via the power variance frailty between the conditional transition rates that are assumed Markov. Inference is conducted via maximum likelihood. A simulation study is conducted to evaluate the finite sample performance of the model parameters. Finally, we compare and contrast our power variance frailty proposal to known alternatives via application to a colon cancer dataset. Relevant R code for implementation of our model is available in GitHub.

Navigating uncharted waters: black swan events and global supply chain resilience

This paper delves into the critical realm of global supply chain management, exploring the challenges posed by black swan events—unpredictable and high-impact occurrences. In navigating these uncharted waters, the focus is on bolstering resilience to ensure the continuity and adaptability of supply chains. The study emphasizes proactive risk assessment and scenario planning, advocating for diversification, redundancy, and the integration of technology. Collaboration, agile supply chain practices, and resilient financial planning are deemed pivotal, along with a commitment to regulatory compliance and employee well-being. The pursuit of continuous improvement and the optimization of global networks round out a comprehensive strategy to fortify supply chains against the unforeseen disruptions that characterize the contemporary business landscape.

Navigating Uncertainties: A Holistic Framework for Identifying and Assessing Risks in Third-Party Logistics (3PL)

To review the extant literature on 3PL risk management (SCRM, including risk identification, assessment, treatment, and monitoring), developing a comprehensive definition and conceptual framework; to evaluate prior theory use; and to identify future research directions. Additionally, our framework emphasizes the importance of aligning performance metrics with risk objectives, facilitating ongoing monitoring and evaluation of 3PL partnerships. By establishing clear performance benchmarks and feedback mechanisms, organizations can promptly identify emerging risks and implement corrective actions as necessary. Methodology: A systematic literature review of couple of articles (published 2000-2016) based on descriptive, thematic, and content analysis. Drawing from extensive literature review and industry best practices, our framework integrates multiple dimensions of risk identification and evaluation into a cohesive methodology. We propose a structured approach that encompasses both qualitative and quantitative techniques, enabling organizations to comprehensively assess risks across various stages of the 3PL relationship lifecycle. Through the application of our holistic framework, organizations can enhance their ability to navigate uncertainties in 3PL relationships, fostering resilience and competitiveness in today's dynamic business environment. Findings: There has been a considerable focus on identifying risk types and proposing risk mitigation strategies. Research has emphasised organisational responses to 3PL risks. Research implications: A broad, contemporary understanding of 3PL risk management is provided; leading to a conceptual framework. The research agenda guides future work towards maturation of the discipline. Practical implications: Managers are encouraged to adopt a holistic approach to risk management. Guidance is provided on how to select appropriate risk treatment actions according to the probability and impact of a risk. Key components of our framework include risk categorization, stakeholder engagement, scenario analysis, and performance metrics alignment. By systematically categorizing risks according to their nature and impact, organizations can prioritize mitigation efforts and allocate resources effectively. Stakeholder engagement ensures that diverse perspectives are considered in the risk assessment process, enhancing its accuracy and relevance. Furthermore, scenario analysis allows for the exploration of potential future events and their implications on 3PL operations, enabling proactive risk management strategies. Keywords: 3PL, Risk management, Systematic literature review (SLR), theoretical approach

Risk assessment for synthetic GICs: a quantitative framework for asset–liability management

AbstractThis study addresses a research gap in quantitative modeling framework and scenario analysis for the risk management of stable value fund wraps, a crucial segment of the U.S. financial market with over USD $400 billion in assets. In this paper, we present an asset–liability model that encompasses an innovative approach to modeling the assets of fixed-income funds coupled with a liability model backed by empirical analysis on a unique data set covering 80% of the stand-alone plan sponsor market, contrasting with models based solely on regular deterministic cash flows and interest rate differences. Our model identifies and analyzes two critical risk scenarios from the insurer’s perspective: inflationary and yield spike. Our approach demonstrates that the tail risk of wraps, used as an economic capital measure, is sensitive to characteristic parameters of the fund, such as the duration, portfolio composition and credit quality of assets. This finding significantly differs from U.S. regulatory approaches like the NAIC’s, which often result in a zero capital requirement. These findings reveal limitations in current actuarial risk and profitability metrics for U.S. insurers and argue that a more sophisticated risk model reproducing the two critical scenarios is necessary.

Challenges for Sustainable Urban Planning: A Spatiotemporal Analysis of Complex Landslide Risk in a Latin American Megacity

This paper analyzes the spatiotemporal evolution of a complex landslide risk scenario in a Latin American megacity, underscoring the key challenges it poses for sustainable urban planning in such cities. This research draws upon multiple studies commissioned by the mayor’s office of the megacity of Bogota, Colombia, and utilizes aerial photographs and satellite imagery from diverse sensor types. The methodology used considered six spatiotemporal analysis scenarios: rural/natural, mining, urban, landslide risk, stabilization and environmental park, and informal reoccupation. The findings reveal a complex interplay between the megacity’s peripheral areas, which face constraints for human settlement, and their potential for construction material exploitation. This complex relationship was further compounded by weaknesses in planning and controlling peripheral occupations, coupled with a burgeoning demand for developable land in a landslide risk context (landslide area: 73 ha). The analysis scenarios highlighted the predominant use of a reactive urban planning approach that addressed events, changes, or problems after they had occurred, rather than proactively anticipating and preventing potential risks at the study site. The detected land-use transformations unveiled different historical moments, culminating in a landslide disaster (804 houses destroyed, 3000 families at risk). This catastrophe necessitated a radical and significant intervention, incurring substantial costs for the megacity administration (USD 26.05 million). This landslide was the largest recorded in the megacity and one of the most extensive in urban areas across Latin America.

An Active Distribution Grid Exceedance Testing and Risk-Planning Simulation Based on Carbon Capture and Multisource Data from the Power Internet of Things

In order to achieve peak carbon and carbon neutrality targets, a high number of distributed power sources have been connected to distribution networks. How to realize the planning of a distribution network containing integrated energy under the condition of carbon capture and complete the exceedance test of the distribution network under the condition of accessing a large number of distributed generators has become an urgent problem. To solve the above problem while promoting sustainable development, this work proposes an active distribution network risk-planning model based on multisource data from carbon capture and the Power Internet of Things. The model calculates the semi-invariants of each order of the node state vectors and branch circuit current vectors and then utilizes Gram–Charlier-level expansion to obtain the exceeding probability density function and the probability distribution functions of the node voltages and line powers in the distribution network. Combined with multisource data, an active distribution network with an integrated energy system designed for carbon capture was modeled. According to the risk scenario of the distribution network, the nonconvex constraints in the model were simplified by second-order cone relaxation, and the optimal planning scheme of the distribution network was solved by combining the Gurobi solver with the risk index as the first-level objective and the economic benefit as the second-level objective. The simulation results of a coupled network consisting of a 39-node distribution network and an 11-node transportation network verified the effectiveness of the proposed model.

A Conceptual Architecture of Fuzzy Risk-Based Decision Support Systems (R+DSS)

This paper introduces a conceptual architecture for Fuzzy Risk-Based Decision Support Systems (R+DSS). This architecture is designed to provide a comprehensive and efficient approach to decision-making procedures in various domains involving assessing and controlling potential risks. The proposed architecture exhibits versatility in its applicability across multiple fields, such as finance, healthcare, engineering, and environmental management. It incorporates these components flexibly and scalable while also being user-friendly. The framework employs fuzzy logic principles such as membership functions, rule sets, and inference methods to facilitate a thorough evaluation of the risk that accommodates the inherent uncertainties and imprecisions characteristic of real-world risk scenarios. The Fuzzy Inference Engine is a versatile and resilient risk analysis tool capable of accommodating diverse data and systems, enabling effective risk mitigation strategies. The adaptability of this architecture to effectively handle complex, uncertain and dynamic environments makes it a promising tool for decision-makers looking to improve risk assessment and management protocols.

Chemical release risk assessment in earthquake: Natech event scenario

IntroductionIn recent decades, an increase in natural disasters has led to the Natech events occurrence, which are technological accidents triggered by natural hazards. This study aims to assess the risk of chemical release of the H2S toxic gas following an earthquake from the refinery. MethodThe Natech risk assessment was carried out in potential earthquake risk scenarios via a semi-quantitative method using Rapid-N software. The reference scenario was obtained using the Australian SMUG Model, which has presented precise prioritization. FindingsThe Natech risk assessment has shown 40 possible earthquake risk scenarios from 5 enclosing Faults in Tehran (North Tehran, Mosha, North Ray, South Ray, and Kahrizak) with an extension of more than 15 km. The earthquake moment magnitudes of 7.5, 7.1, 6.5, and 5.9 were obtained on the Richter scale and at two Focal depths of 5 and 10 km. The South-Ray Fault (HSR-1) was selected as the reference scenario with a moment magnitude of 7.5 on the Richter scale at a Focal depth of 5 km and a distance of 5.5 km from the earthquake epicenter. The highest probability of Natech risk has shown the release of H2S toxic gas in an area with a radius of 6.59 km from the studied vessel in atmospheric stability conditions. ConclusionLegislation in line with risk reduction, planning for the transfer of potentially dangerous industries to outside urban areas, monitoring land use laws, and promoting coordination between the government, industries, and people in the management of risks caused by industries containing hazardous substances and processes, assessing the risk of Natech events and their consequences, strengthening risk communication and the installation of early warning systems, and public education to adopt personal and socially safe behaviors when facing natural disasters are recommended.

Systems analysis for energy assets of Iraq influenced by water scarcity

Water security in arid and semi-arid Middle Eastern climates has been severely impacted by effects of climate change such as reduced precipitation, diminished storage, increased evapotranspiration, and prolonged heat waves. These climate effects are compounded in Iraq, where populations, agriculture, industry, and energy rely heavily on varying transboundary water flows to meet water demands. Iraq’s most profitable sector, energy, is especially threatened by insufficient water supply, which complicates government decision making in energy infrastructure development. The goal of this paper is to develop a scenario-based multi-criteria analysis framework to prioritize infrastructure investments in the context of climate change and scarcity of natural resources. Infrastructure facilities are evaluated against social, economic, climate, and hydrologic criteria across a set of disruptive climatological, economic, and social scenarios to identify robust initiatives and the most and least disruptive scenarios to the system. A particular innovation of this paper is the evaluation of hydrology data derived from satellites in determining water scarcity impact on individual energy facilities. The methods are demonstrated for a critical sector of Iraq’s economy: oil and natural gas. The demonstration includes 13 system-order criteria, 44 oil and gas initiatives within Iraq, and seven risk scenarios. The results include an accounting of the most and least disruptive scenarios to energy sector priorities and scenario-based system orderings to guide stakeholders in investment prioritization.

Beta functions adaptation on empirical damage for a fragility model of Italian buildings

In this study, heuristic vulnerability model is presented providing damage distribution as a function of ground motion severity, through the use of given distribution function.Firstly, observational damage distribution are obtained from data collected after the recent earthquakes occurred in Italy and made available by DPC through Da.D.O. platform. The latter collect information on the main characteristics about building position, metrical data (e.g., number of storeys, interstorey height, storey area and construction age), structural types, severity and extension of observed damage on different building components. Moreover, the ground motion characterization was described via the ShakeMap published by the National Institute of Geophysics and Volcanology.Then, the two statistical models mostly used in the literature, i.e. binomial and beta, are used to determine the one most suitable to reproduce the observational damage distributions by means of godness-of-fit test. An introductive section properly examine the pro and cons in their use (number of parameters, flexibility). The observation of mean value and variance couple for more than 2000 damage samples obtained based on the considered database is used to obtain a general relationship which allows to reduce the number of parameters required by beta distribution, preserving its renomate flexibility.This enables the formulation of a novel heuristic vulnerability model for derivation of fragility curves. The model relies on the knoledge of mean damage trend with intensity measure and employs a probability distribution function. In this study, specifically, binomial and beta distributions are utilized. The generality of the method is then customized to suit the characteristics of Italian residential building stock, leveraging the information gathered after the 2009 L'Aquila Earthquake. This database presented an unprecedented opportunity due to the extensive data available on building geometrical and typological characteristics, seismic vulnerability, to the high level of representativeness of samples at municipality level, and to the thorough representation of damage patterns observed on building components.The fragility curves obtained through the presented vulnerability model are compared with those empirically derived from the observational data and are used to fully derive seismic risk scenario after 2009 L'Aquila Earthquake, in terms of economic losses, number of long term unusable buildings, the number of collapse, the number of deaths and severely injuries. The ensuing consequences deriving from the adoption of different fragility curves, i.e. empirical or with beta and binomial vulnerability models are deeply investigated.

NAVIGATING NON-TECHNICAL RISKS IN THE OIL & GAS INDUSTRY: INSIGHTS AND FRAMEWORKS - A REVIEW

Non-technical risks play a critical role in the oil and gas industry, influencing operational efficiency, financial performance, and stakeholder trust. This review explores key insights and frameworks for understanding and managing non-technical risks in the oil and gas sector. The review begins by defining non-technical risks and highlighting their significance in the industry. It then discusses various types of non-technical risks, including regulatory, environmental, social, and geopolitical risks, and their impact on oil and gas operations. The review also examines the interconnected nature of non-technical risks and how they can escalate into larger crises if not managed effectively. It emphasizes the importance of adopting a holistic approach to risk management that considers the interplay between different risk factors. Furthermore, the review identifies several key frameworks and methodologies for assessing and managing non-technical risks in the oil and gas industry. These include risk assessment tools, scenario planning, and stakeholder engagement strategies. The review concludes by highlighting the need for oil and gas companies to proactively identify, assess, and mitigate non-technical risks. It emphasizes the importance of integrating risk management into overall business strategies and decision-making processes. Overall, this review provides valuable insights and frameworks for navigating non-technical risks in the oil and gas industry. It offers practical recommendations for industry practitioners and policymakers to enhance risk management practices and ensure the long-term sustainability of the sector.
 Keywords: Oil and Gas, Insights, Frameworks, Navigating, Non- Technical Risks

Identifying undefined risks: A risk model and a privacy risk identification measure in the privacy impact assessment process

Privacy impact assessment (PIA) has attracted the attention of privacy watchdogs and researchers for decades. This study focuses on a risk model and risk identification method, which are two crucial elements of the risk identification step in the PIA process. As a preparatory work, this article reviews national and international organizations’ current templates and guidelines and finds that PIA guidance includes multiple domains but rarely provide a risk model or a systematic risk identification method. Based on the analysis, our study offers a risk model that can capture various privacy risk realization processes. It further proposes a combination of risk identification methods that correspond to the main target domains in the PIA and the proposed risk model. This combination consists of privacy principles of a given personal information or privacy rule to check compliance with the rule, and our suggested list of risk factors is useful in inductively finding potential risk scenarios that violate social expectations of privacy.

Development and Reliability Review of an Assessment Tool to Measure Competency in the Seven Elements of the Risk Management Process: Part Three—Evaluation of the Group Results from the RISK Tool

This study used ratings to form teams of participants with different risk management competence levels to determine if a collectively optimised team performed a risk management exercise better than a marginally or a sub-optimised team. This paper also determined whether team performance was better than individual performance on a risk management exercise. An experimental group was split into three teams of six participants based on their individual risk scenario exercise outcomes. The collectively optimised team had at least one member rated as having some high-level or expert competency in one of the seven risk management process elements. So, jointly, the group had this competency level in all elements. Similarly, the marginally optimised team’s members were rated as having just above average or high-level competency in the seven elements. Likewise, the sub-optimised team’s members were rated as having just above average competency, just below average, or no competency in the seven elements. Each team undertook the risk scenario exercise, and two observers rated their performances, as recorded on a video camera. The results were that the collectively optimised team performed better in each of the seven risk management elements than the other teams (the marginally optimised or the sub-optimised team). However, a significant difference was only evident between the collectively optimised and sub-optimised teams across all elements. Also, the teams performed better in each of the seven elements than individuals. These results imply that a team collectively optimised in the seven elements of the risk management process can better perform a risk management process than a sub-optimised team. These competency outcomes could be used to assemble risk management teams that are collectively optimised, leading to better results from the risk management process.

AI Risk Assessment: A Scenario-Based, Proportional Methodology for the AI Act

The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with a framework developed by the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers.

The Role of The State in Diversifying and Expanding the Brazilian Energy Matrix: an Analysis of Legislation

Despite having an essentially renewable electricity matrix, the dependence on hydropower, in the current climate risk scenario and the associated environmental and social impacts, point to the need to diversify energy generation in Brazil. To this end, state action is fundamental for designing and implementing public policies for the country's energy matrix. However, this diversification does not occur at the desired speed and scale. Thus, this article analyses the Brazilian regulatory environmental policies to assess the participation of the Federal constituencies, States, and municipalities in scaling renewable sources in the national electricity matrix. The research revealed that specific legislation on energy is scarce, and much of what is applied is taken from the general environmental legislation. We noticed that, by constitutional imposition, the Federal constituencies have the legislative competence on the theme and, therefore, establish norms and general rules, while States and municipalities act marginally, especially in fiscal matters and environmental licensing. The need for more objectivity, standardization, and the non-existence of specific technical criteria in the norms about energy use. We also verified that most of what was legally established constitutes government programs, not state policies. It is necessary to extend the participation of states and Municipalities in elaborating national energy use plans and in the revision and technical standardization of the licensing norms. These gaps must be filled so sustainable development, a principle established in the Constitution, is strengthened and made possible, with the participation of States and Municipalities.

A Robust and Risk-Averse Medical Waste Chain Network Design by considering Viability requirements

This research suggests a Robust and Risk-Averse Medical Waste Chain Network Design by considering Viability requirements (RRMWCNDV). The aim is to locate a waste management facility that minimizes waste and promotes the recycling of materials like metal and plastic, contributing to environmental benefits. The proposed RRMWCNDV aims to be viable, robust and risk-averse. A two-stage robust stochastic programming model was utilized to develop this framework. It incorporates risk by employing the Weighted Value at Risk (WVaR) approach for the first time. The study reveals that incorporating risk and robustness scenarios results in a lower cost function. The degree of conservatism in decision-making can be adjusted between 0% and 100%, increasing the cost function. The confidence level in WVaR indicates risk aversion, with an increase in the cost function with a 4% increase. The agility coefficient, which indicates the percentage of waste demand production from HC transferred to another facility, also affects the cost function and population risk. A decrease in the sustainability coefficient results in a 53% rise in the cost function and a 12.82% increase in population risk. The model demonstrates NP-hard characteristics and becomes exponentially complex for larger scales.

Proposed life cycle assessment approach to estimate the environmental impact due to high altitude platform systems

The anthropic activities, starting from the early industrial development phases, are pointed-out as concurrent causes affecting the environmental conditions of planet Earth as well as people life quality. Evidences of these influences have been observed even in the remote territories like North and South poles. Every day news report about extreme meteorological events affecting vast geographical areas with heavy loss in terms of victims and damages to infrastructures. In these potential risk scenarios, it is extremely important to support emergency management with timely and accurate geo-spatial information and also to restore minimal communication services. In these severe conditions a relevant role may be played by High-Altitude Platform Systems (HAPS) operating at high altitudes, ranging from 17 to 22 kilometres, typically in the stratosphere, extending coverage for large areas and improving connectivity in remote or underserved areas, bridging the digital divide and providing connectivity to rural or isolated regions, carrying remote sensing instruments, such as imaging sensors or atmospheric sensors, to gather data about the Earth’s surface, weather patterns, or atmospheric conditions. To analyse their sustainability, the proposed work presents the definition of a Life Cycle Assessment (LCA) for two distinct “lighter than air” configurations: an “innovative blimp airship” (IBA) and a classical blimp airship (CBA) as baseline. While IBA configuration is considered as an unmanned, the CBA airship is a manned configuration. The goal of this activity is to support the characterization and validation of the environmental contribution in terms of CO2 emissions due to innovative technologies, concepts and industrial processes adopted for the development of such HAPS configurations. For this assessment SimaPro tool has been used. The applied methodology has adopted the ISO standards: ISO-14040:2006 and ISO-14044:2018 as guidelines to estimate the collectable benefits due to the adoption of innovative eco-design solutions. Specifically, this paper is focused on the identification of materials and on inventory development in order to support the LCA process.

The allergenic potential of green urban areas in the Macaronesian islands: The case of Funchal City (Madeira)

Public gardens are elements that constitute the urban green infrastructure. Such units provide ecosystem services that are becoming a key concern in urban planning strategies.In this survey the allergenic potential of two public gardens in Funchal, a city exhibiting sub-tropical and Mediterranean climatic features, was assessed. Different allergenic risk scenarios were considered, and the Allergenicity Indexes recalculated. Results show that the Municipal Garden exhibits an IUGZA (Index of Urban Green Zone Allergenicity) of 0,39 and Santa Catarina Park an IUGZA of 0,16, which are considered spaces of moderate and low allergenicity level, respectively. Among the tested scenarios that could aggravate the IUGZA values, the worst would be an extension of the pollen season for all species. This might represent an issue considering the overall trend towards an earlier start and later end of the pollen season from important allergenic plants in Europe. The assessment of the IUGZA should include all plant biotypes of an urban green area in subtropical regions as they allow us to infer more precisely on the real risk population is exposed to. It seems that increasing species diversity can improve the allergenic indexes, but novel invasive species should be studied to assess their allergenicity level.

Extending the code in the open-source saemix package to fit joint models of longitudinal and time-to-event data

Background and ObjectiveJoint modeling of longitudinal and time-to-event data has gained attention over recent years with extensive developments including nonlinear models for longitudinal outcomes and flexible time-to-event models for survival outcomes, possibly involving competing risks. However, in popular software such as R, the function used to describe the biomarker dynamic is mainly linear in the parameters, and the survival submodel relies on pre-implemented functions (exponential, Weibull, ...). The objective of this work is to extend the code from the saemix package (version 3.1 on CRAN) to fit parametric joint models where longitudinal submodels are not necessary linear in their parameters, with full user control over the model function. MethodsWe used the saemix package, designed to fit nonlinear mixed-effects models (NLMEM) through the Stochastic Approximation Expectation Maximization (SAEM) algorithm, and extended the main functions to joint model estimation. To compute standard errors (SE) of parameter estimates, we implemented a recently developed stochastic algorithm. A simulation study was proposed to assess (i) the performances of parameter estimation, (ii) the SE computation and (iii) the type I error when testing independence between the two submodels. Four joint models were considered in the simulation study, combining a linear or nonlinear mixed-effects model for the longitudinal submodel, with a single terminal event or a competing risk model. ResultsFor all simulation scenarios, parameters were precisely and accurately estimated with low bias and uncertainty. For complex joint models (with NLMEM), increasing the number of chains of the algorithm was necessary to reduce bias, but earlier censoring in the competing risk scenario still challenged the estimation. The empirical SE of parameters obtained over all simulations were very close to those computed with the stochastic algorithm. For more complex joint models (involving NLMEM), some estimates of random effects variances had higher uncertainty and their SE were moderately under-estimated. Finally, type I error was controlled for each joint model. Conclusionssaemix is a flexible open-source package and we adapted it to fit complex parametric joint models that may not be estimated using standard tools. Code and examples to help users get started are freely available on Github.

Eliciting expert judgements to underpin our understanding of faecal indicator organism loss from septic tank systems

Septic tank systems (STS) in rural catchments represent a potential source of microbial pollution to watercourses; however, data concerning the risk of faecal indicator organism (FIO) export from STS to surface waters are scarce. In the absence of empirical data, elicitation of expert judgements can provide an alternative approach to aid understanding of FIO pollution risk from STS. Our study employed a structured elicitation process using the Sheffield Elicitation Framework to obtain expert judgements on the proportion of FIOs likely to be delivered from STS to watercourses, based on 36 scenarios combining: (i) septic tank effluent movement risk, driven by soil hydro-morphological characteristics; (ii) distance of septic tank to watercourse; and (iii) degree of slope. Experts used the tertile method to elicit a range of values representing their beliefs of the proportion of FIOs likely to be delivered to a watercourse for each scenario. The experts judged that 93 % of FIOs would likely be delivered from an STS to a watercourse under the highest risk scenario that combined (i) very high STS effluent movement risk, (ii) STS distance to watercourse <10 m, and (iii) a location on a steep slope with gradient >25 %. Under the lowest risk scenario, the proportion of FIOs reaching a watercourse would likely reduce to 5 %. Expert confidence was high for scenarios that represented extremes of risk, while uncertainty increased for scenarios depicting intermediate risk conditions. The behavioural aggregation process employed to obtain a consensus among the experts proved to be useful for highlighting both areas of strong consensus and high uncertainty. The latter therefore represent priorities for future empirical research to further improve our understanding of potential pollution risk from septic tanks and in turn enable better assessments of potential threats to water quality in rural catchments throughout the world where decentralised wastewater systems are common.