Risk Mitigation Research Articles

Source-specific ecological and human health risk analysis of topsoil heavy metals in urban greenspace: a case study from Tianshui City, northwest China.

Soil contamination of heavy metals in urban greenspaces can exert detrimental impacts on ecological biodiversity and the health of inhabitants through cross-media migration-induced risks. Here, a total of 72 topsoil samples were collected from greenspaces in the popular tourist city of Tianshui, ranging from areas with parks, residential, road, industrial and educational soils. The study aimed to evaluate an integrated source-specific ecological and human health risk assessment of heavy metals. Among the analyzed heavy metals, except Cr (mean), all exceeded the local background values by 1.30-5.67-fold, and Hg, Cd, Pb and As were the metals with large CV values. The Igeo and CF results showed Hg, Cd, As and Pb exhibited significantly high pollution levels and were the primary pollution factors. The mean PLI values indicated moderate pollution in educational (2.21), industrial (2.07), and road (2.02) soils but slight pollution in park (1.84) and residential (1.39) greenspaces. The Igeo, CF, and PLI results also revealing that these heavy metals are more likely to be affected by human activity. Four primary source factors were identified based on PMF model: coal combustion (25.57%), agricultural sources (14.49%), atmospheric deposition (20.44%) and mixed sources (39.50%). In terms of ecological risk, the mean IRI values showed considerable risks in educational soils (287.52) and moderate risks in road (215.09), park (151.27) and residential (136.71) soils. And the contribution ratio of atmospheric deposition for park, residential, road, industrial and educational greenspaces were 57.72%, 65.41%, 67.69%, 59.60% and 75.76%, respectively. In terms of human health risk, the HI (below 1) and CR (below 1.00E-04) for adults from soils of all land use types was negligible. However, children have more significant non-carcinogenic and carcinogenic hazards especially in residential soils, the HI (above 1) and CR (above 1.00E-04) revealed the significance of regarding legacy As contamination from coal combustion when formulating risk mitigation strategies in this area. The proposed method for source and risk identification makes the multifaceted concerns of pollution and the different relevant risks into a concrete decision-making process, providing robust support for soil contamination control.

Enhancing supply chain resilience through risk mitigation strategies: Evidence from smallholder red chili supply chains in East Java Indonesia

The cultivation of red chili in East Java, Indonesia, has significant economic and social impacts, necessitating proactive supply chain measures. This research aimed to identify priority risk agents, develop effective risk mitigation, and enhance supply chain resilience using the SCOR model, House of Risk, Interpretative Structural Modelling (ISM), and synthesis analysis. Examining 238 respondents—including farmers, collectors, wholesalers, retailers, home-agroindustries, and experts—the findings highlight farmers’ critical role in supply chain resilience despite risks from crop failures, weather fluctuations, and pest infestations. Simultaneous planting led to market oversupply and price drops, but accurate pricing information facilitated quick market adaptation. Wholesalers influenced pricing dynamics and income levels, impacting farmers directly. To improve resilience, three main strategies were developed through ten key elements: proactive strategies (real-time SCM tracking, Weather Early Warning Systems, risk management team formation, and training), resistance strategies (partnerships, chili stock reserves, storage and drying technologies, GAP implementation, post-harvest management, agricultural insurance, and Fair Profit Sharing Agreements), and recovery and growth strategies (flexible distribution channels and customizable distribution centers). Furthermore, the study delves into the mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also provides stakeholders with evidence to consider new strategies to enhance red chili supply resilience.

Supply chain chronic losses and risk interdependencies: a Bayesian Belief Network approach

ABSTRACT Supply Chain Risk Management (SCRM) has become integral to firm governance, and risk interdependencies, despite limited attention, significantly impact risk analysis and mitigation. Recurrent risks, resulting from consistent disturbances, generate chronic losses and are critically affected by other related risks. This work enhances the assessment of chronic losses with interdependent risks, building upon an existing Bayesian Belief Network (BBN) approach for risk interdependencies in SCRM, combining expected utility theory to address the complex, interconnected nature of risks. This work advocates a change in existing approaches to properly evaluate chronic losses often underestimated due to decision makers’ expectations. The research shows that the impact of recurrent risks may be underestimated by other approaches based on their frequency, undermining confidence in mitigation strategies. This work not only presents a novel approach to risk assessment but also highlights the importance of studying causal interdependencies, and emphasises the need to consider low-probability and high-impact risks but also recurrent, seemingly minor risks in SCRM processes.

Risk assessment of public-private partnership water projects in Indonesia

Public-private partnerships (PPPs) are vital for infrastructure development in developing countries, integrating private efficiency with public oversight. However, PPP models often face risks, particularly in Indonesia’s water sector, due to its unique geographical and regulatory challenges. This study aims to identify and evaluate risk factors specific to drinking water PPP projects in Indonesia. Using a quantitative approach, structured questionnaires were distributed to experts in the sector, and the data was analyzed using a fuzzy evaluation method. Risks were categorized into location, design and construction, financial, operational, revenue, and political. The study emphasizes that effective risk management, including identification, analysis, and mitigation, is essential for project success. It highlights the importance of stakeholder involvement and flexible risk management strategies. Comprehensive and proactive risk management is key to the success of drinking water infrastructure projects. The research suggests that an integrated and collaborative approach among stakeholders can enhance risk management effectiveness. These findings provide valuable insights for policymakers, project managers, investors, and other stakeholders, underscoring the necessity for adaptable regulatory frameworks and robust policy guidelines to improve the sustainability and efficacy of future water-related PPPs.

Effect of Financial Metrics on Risk Indicators in Nigeria Listed Deposit Money Banks

This study investigates the effect of financial metrics on risk indicators in Nigerian deposit money banks. The analysis employs yearly time series data spanning from 2007 to 2022, acquired from the Exchange Group PLC. Descriptive statistics, panel unit root tests, Hausman tests, and Panel Ordinary Least Squares (OLS) procedures were used at a 95% confidence interval. The study utilized secondary data sourced from the Exchange Group PLC database. The R-squared (0.564390) and Adjusted R-squared (0.540629) values indicate that the models have strong explanatory power. The results show that all variables are stationary at their levels (I(0)). The primary financial metrics influencing risk indicators among deposit money banks in Nigeria are revenue growth, net interest margin, and earnings per share. It was recommended that banks should implement effective risk management systems that can handle increased complexity and scale of operations, and regularly update them, leveraging blockchain technology for decentralized risk management as it relates to revenue growth rate of banks. Maintain a healthy net interest margin through effective risk management practices and internal controls, and utilize this strength to invest in risk mitigation measures, introducing incentive programs to encourage employee involvement in risk management. Conduct regular financial reviews and audits to ensure accurate earnings reporting and risk identification, utilizing AI-powered tools for earnings analysis to identify anomalies and potential risks. Prioritize prudent lending practices and effective risk management to maintain financial stability, implementing dynamic adjustments to the debt-to-equity ratio in response to changes in risk detection needs.

Incorporating Effects of Slope Units and Sliding Areas into Seismically Induced Landslide Risk Modeling in Tectonically Active Mountainous Areas

Traditional Newmark models estimate earthquake-induced landslide hazards by calculating permanent displacements exceeding the critical acceleration, which is determined from static factors of safety and hillslope geometries. However, these studies typically predict the potential landslide mass only for the source area, rather than the entire landslide zone, which includes both the source and sliding/depositional areas. In this study, we present a modified Newmark Runout model that incorporates sliding and depositional areas to improve the estimation of landslide chain risks. This model defines the landslide runout as the direction from the source area to the nearest river channel within the same slope unit, simulating natural landslide behavior under gravitational effects, which enables the prediction of the entire landslide zone. We applied the model to a subset of the Minjiang Catchment affected by the 1933 MW 7.3 Diexi Earthquake in China to assess long-term landslide chain risks. The results indicate that the predicted total landslide zone closely matches that of the Xinmo Landslide that occurred on 24 June 2017, despite some uncertainties in the sliding direction caused by the old landslide along the sliding path. Distance-weighted kernel density analysis was used to reduce the prediction uncertainties. The hazard levels of the buildings and roads were determined by the distance to the nearest entire landslide zone, thereby assessing the landslide risk. The landslide dam risks were estimated using the kernel density module for channels blocked by the predicted landslides, modeling intersections of the total landslide zone and the channels. High-risk landslide dam zones spatially correspond to the locations of the knickpoints primarily induced by landslide dams, validating the model’s accuracy. These analyses demonstrate the effectiveness of the presented model for Newmark-based landslide risk estimations, with implications for geohazard chain risk assessments, risk mitigation, and land use planning and management.

A risk assessment of a gas pressure reduction station system with confidence for dealing with imprecisions and unknowns

Process systems are sensitive and vital industrial facilities. Disturbances in their performance may cause harm to the environment,humans,or significant economic damage. In risk assessment of chemical process industries, the available data, information, and knowledge are typically rare, limited, and often unrealistic. This issue poses a challenge to conducting a credible quantitative risk assessment and effects the robustness of the results. To address these challenges, this work proposes a methodology based on the Dempster-Shafer theory of evidence as the reasoning framework. It incorporates risk identification, analysis, and mitigation phases to ensure a thorough analysis of risks and the integration of proactive risk reduction strategies. The approach aims to model the worst-case hazard scenario and assess associated risks using various methods such as FMECA, Bow-Tie, Credal Network, and Dempster-Shafer theory. The proposed approach models imprecision and data ambiguity using intervals and associated belief mass. This extension provides a basis for addressing the fundamental problem of prior ignorance about the distribution of the observed data, which is prevalent in data mining applications. A new approach is proposed that utilizes Belief and Plausibility curves, similar to a Cumulative Distribution Function, to propagate uncertainty, enhance criticality discrimination, and determine cumulated belief measures. This approach is applied in analyzing the failure modes identified in FMECA and is further extended through the credal network for comprehensive risk assessment. Results show how to express irrelevant and independent judgments, and how to work out with inferences in credal networks. This issue is often overlooked, but if properly addressed it represents the key to ultimately drawing reliable conclusions and fully utilizing the system's available data. A case study of the City Gate Station system was used to verify the application potential of the proposed approach.

Dynamic Characteristics of the Long Runout Rock‐ice Avalanche at High Altitude—A Case from the Zelongnong Basin, Eastern Himalayan Syntaxis, China

AbstractRock‐ice avalanches have frequently occurred in the Eastern Himalayan Syntaxis region due to climate change and active tectonic movements. These events commonly trigger catastrophic geohazard chains, including debris flows, river blockages, and floods. This study focuses on the Zelongnong Basin, analyzing the geomorphic and dynamic characteristics of high‐altitude disasters. The basin exhibits typical vertical zonation, with disaster sources initiating at elevations exceeding 4000 m and runout distances reaching up to 10 km. The disaster chain movement involves complex dynamic effects, including impact disintegration, soil‐rock mixture arching, dynamic erosion, and debris deposition, enhancing understanding of the flow behavior and dynamic characteristics of rock‐ice avalanches. The presence of ice significantly increases mobility due to lubrication and frictional melting. In the disaster event of September 10, 2020, the maximum flow velocity and thickness reached 40 m/s and 43 m, respectively. Furthermore, continuous deformation of the Zelongnong glacier moraine was observed, with maximum cumulative deformations of 44.68 m in the distance direction and 25.96 m in the azimuth direction from March 25, 2022, to August 25, 2022. In the future, the risk of rock‐ice avalanches in the Eastern Himalayan Syntaxis region will remain extremely high, necessitating a focus on early warning and risk mitigation strategies for such basin disasters.

Retraction Note: Green financing and environmental risk mitigation: is environmental protection tax law that important?

Retraction Note: Green financing and environmental risk mitigation: is environmental protection tax law that important?

An ISM-MICMAC-based study for identification and classification of preventable safety risk mitigation factors in mass housing projects following a BIM approach

Construction operation is among the most high-risk sectors in terms of work-related accident, making it highly challenging to surveil the safety of such projects. In construction projects, failure to observe safety represents a leading cause of fatal accidents, not to mention the losses incurred by such accidents to national assets of the country. Accordingly, recent decades have witnessed the emergence of modern techniques for improving the occupational safety of construction projects. The main purpose of the present research is to identify and classify different preventable risk mitigation factors in mass housing projects following a building information modeling (BIM) approach. The research methodology included interviews with relevant experts and elites followed by analysis of the data on the 12 identified-as-significant variables for mitigating the preventable risk factors in mass house construction projects by means of the inferential – structural modeling (ISM) in MICMAC software. In order to explore the relationships among and succession of different criteria and further classify them at different levels, ISM was implemented, with the MICMAC software used to analyze the direct and indirect influences, develop influence/dependence maps, and judge about the role of each criterion. Findings of the present research showed that the mutual relations (H3), the reward system (H6), the reporting system (H7), and the supervisors' supervision (H8) are autonomous variables and hence impose the smallest contributions to the system. Accordingly, they can be eliminated from the model though their effects may not be completely ignored. On the other hand, the employees’ empowering (H4), the safety management system (H5), the teamwork (H9), the self-efficiency (H10), and the knowledge and awareness (H11) were identified as the linkage variables that fill in the gap between the safety and occupational accident reduction in the mass house construction projects. Further, the continuous improvement (H2) and the safe behavior (H12) were identified as dependent variables, implying that they exhibit the weakest influence coupled with highest dependence on any change in the conditions of the system. Last but not the least, the management commitment (H1) was identified as the only dependent variable which deserves lots of attention. This information can be helpful to safety decision-makers, end users, research organizations, and academic institutes who work to reduce the preventable risk factors in mass house construction projects.

Globalization and Local Procurement: A Study of Supply Chain Dynamics in Australia and Bangladesh

Purpose: The aim of the study was to analyze the globalization and local procurement: a study of supply chain dynamics in Australia and Bangladesh. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study on strategic sourcing's impact on cost reduction in Australia and Bangladesh manufacturing firms demonstrated significant positive results. It showed that strategic sourcing led to notable reductions in procurement costs, enhanced supplier management, negotiation, and risk mitigation strategies. The findings underscored the importance of aligning procurement strategies with organizational goals for sustainable cost reductions and competitive advantages in the sector. Unique Contribution to Theory, Practice and Policy: Resource dependence theory (RDT), transaction cost economics (TCE) & institutional theory may be used to anchor future studies on analyze the globalization and local procurement: a study of supply chain dynamics in Australia and Bangladesh. Businesses in both Australia and Bangladesh should prioritize building strong relationships with local suppliers to enhance supply chain resilience. Policymakers in both Australia and Bangladesh should create supportive regulatory frameworks that promote local procurement practices.

Method "Monte Carlo" in healthcare.

In public health, simulation modeling stands as an invaluable asset, enabling the evaluation of new systems without their physical implementation, experimentation with existing systems without operational adjustments, and testing system limits without real-world repercussions. In simulation modeling, the Monte Carlo method emerges as a powerful yet underutilized tool. Although the Monte Carlo method has not yet gained widespread prominence in healthcare, its technological capabilities hold promise for substantial cost reduction and risk mitigation. In this review article, we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts. We underscore the significance of experiential insights derived from simulated experimentation, especially in resource-constrained scenarios where time, financial constraints, and limited resources necessitate innovative and efficient approaches. As public health faces increasing challenges, incorporating the Monte Carlo method presents an opportunity for enhanced system construction, analysis, and evaluation.

Variational Autoencoders and Generative Adversarial Networks for Multivariate Scenario Generation

When making decisions with lasting implications over a medium to long timeframe, it is essential to consider not only the most probable scenario, possibly obtained through a forecasting model, but also a range of potential outcomes. This approach allows for effective risk mitigation across a spectrum of scenarios, including less probable ones, and enhances the resilience of planning strategies. In this paper, we demonstrate the development of a generative model capable of learning the multivariate joint probability distribution of link speeds on a road network, using real sensor data. To further enhance the performance of our Generative Adversarial Network model, we employed a Variational AutoEncoder for pre-training the generator network. Experimental results, conducted on three distinct benchmark datasets, highlight the potential of the proposed model in generating new scenario samples of multivariate variables. The Wasserstein distance between the generated distribution and the real data, confirms the good performance of our model compared to state-of-the-art models, based on copulae. The proposed model has shown its ability to generate scenarios that preserve correlations among variables, while producing samples that faithfully represent the empirical marginal distributions.

Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction

Earthquakes are natural events caused by the movement of the earth's plates, often triggered by the energy release from hot liquid magma. Predicting earthquakes is crucial for raising public awareness and preparedness in seismically active areas. This study aims to predict earthquake activity by identifying patterns in seismic events using Sequential Pattern Mining (SPM). To enhance the prediction accuracy, Sequential Rule Mining (SRM) is applied to derive rules with confidence values from these patterns. The results show that using betweenness centrality as a weight increases the prediction accuracy to 83.940%, compared to 78.625% without weights. Using eigenvector centrality as a weight yields an accuracy of 83.605%. These findings highlight the potential of using centrality measures to improve earthquake prediction systems, offering valuable insights for disaster preparedness and risk mitigation.

Hot spots for risk-based planning in Greece – the cases of floods and forest fires

ABSTRACT Recent flood and forest fire disasters in Greece highlight the local spatial development history and territorial vulnerability as principal causes. However, despite the strong spatial aspects of disaster risk, disaster management rarely finds its way into spatial planning. The article attempts to locate high disaster risk areas (hot-spots) in Greece and activate risk-based planning. The approach combines theoretical assumptions on the spatial dimension of disaster risk; macro-scale hazard zoning at the national-regional level through past disaster event mapping; meso-scale territorial vulnerability analysis to delimit disaster risk hot-spots; and discussion on statutory planning tools and scales of intervention for risk mitigation.

Big data analytics capabilities and leadership: catalysts of firm performance in telecommunications

PurposeThe proliferation of big data analytics (BDA)-enabled tools and technologies has endowed organizations with the capacity to augment decision-making processes, optimize operational endeavors and foster innovation across diverse business domains. Consequently, BDA has been posited as a catalyst for enhanced customer relationship management, improved risk mitigation strategies and heightened operational efficiencies, all of which converge to augment overall firm performance. Thus, the purpose of this research is to introduce a conceptual framework aimed at explaining the influence of BDA capabilities on the performance of telecommunications firms in Pakistan. Additionally, it examines the potential mediating effect of talent capabilities and moderating effect of top management attitude on firm performance.Design/methodology/approachData from a sample comprising 520 participants were collected via survey questionnaires. The study employed Partial Least Squares-Structural Equation Modeling to empirically evaluate the proposed model.FindingsResults reveal a positive association between BDA technology and information capabilities with both BDA talent capabilities and firm performance. Furthermore, the analysis suggests that BDA talent capabilities mediate the relationship between BDA dynamic capabilities and firm performance, while top management attitude acts as a moderator, enhancing the relationship between BDA talent capabilities and firm performance.Originality/valueThere is a scarcity of research that has examined the relationship of BDA capabilities, top management attitude and firm performance. This study attempts to examine their interrelationships. First, it enhances the extant literature by elucidating the mediating role of BDA talent capabilities in the relationship between BDA technology and information capabilities and firm performance. Second, the study introduces a novel dimension by incorporating top management attitude as a moderator variable. This augmentation adds layers of complexity to comprehending BDA implementation dynamics, emphasizing leadership’s role in fostering an enabling environment for effective utilization of BDA capabilities.

The Role of Scenario-Building in Risk Assessment and Decision-Making on Urban Water Reuse

Urban resilience and water resilience are both increasingly relying on urban non-potable water reuse under the context of the Climate Emergency, but sound risk assessment is lacking. Compared to the state of art, the proposed framework for health risk assessment and management of urban non-potable water reuse includes (i) an additional step for establishing the context and (ii) the risk identification step being extended to introduce a description of the activities from which the hazard exposure scenarios may be built. This novel scenario-building process allows for a clear and comprehensive risk description, assessment, and treatment. The model of risk management is structured around three primary components: the decision-makers, i.e., the municipal services and the population at risk (users and workers); data elements relevant for the risk management process (reclaimed water quality, hazards, hazardous events, sites where exposure can happen, exposure routes, and activities developed by the population at risk and their vulnerabilities); and the links between the decision-makers and these elements and between the elements themselves. Its application in a representative case study shows that the framework comprehensively guides decision-making and communication to relevant stakeholders. From this practical exercise, the main recommendations were derived for risk mitigation by the municipal risk manager and the park users.

Using the moral intensity framework to examine whether colleges should have reopened in late 2020 for in-person learning: an in-depth analysis of data from the United States with global implications for higher education

ABSTRACT The decision whether to reopen universities for in-person learning in late 2020 relied on ethical decision-making where the consequences were dire to the mission of the institution, health of the community, financial well-being of the institution and employees, and had political ramifications. The risks that universities faced worldwide included health risks to students, faculty, staff, and the community; financial risks to the institution and employees; and mission risks of the institution. Each of these risks had ethical implications. University reopening decisions were made in the United States as the number of COVID-19 cases and deaths were surging globally. Applying a moral intensity framework that assesses the in-person learning decision against six dimensions and using data collected from 62 universities, we show that social consensus and risk mitigation measures designed to reduce the magnitude of consequences and temporal immediacy were important factors in deciding to offer in-person classes. An ex-post facto analysis of COVID-19 infection and death data suggests that university administrators made an ethical decision. When facing a similar crisis management scenario in the future, a moral intensity framework could help university administrators worldwide understand the issues and allow them to make an ethical decision.

Enhanced Rock Weathering for Carbon Removal-Monitoring and Mitigating Potential Environmental Impacts on Agricultural Land.

Terrestrial enhanced rock weathering (ERW) is the application of pulverized silicate rock to soils for the purposes of carbon removal and improved soil health. Although a geochemical modeling framework for ERW in soils is emerging, there is a scarcity of experimental and field trial data exploring potential environmental impacts, risks, and monitoring strategies associated with this practice. This paper identifies potential negative consequences and positive cobenefits of ERW scale-up and suggests mitigation and monitoring strategies. To do so, we examined literature on not only ERW but also industry, agriculture, ecosystem science, water chemistry, and human health. From this work, we develop recommendations for future research, infrastructure, and policy needs. We also recommend target metrics, risk mitigation strategies, and best practices for monitoring that will permit early detection and prevention of negative environmental impacts.

Increasing Access to Naloxone in Veterans Receiving Opioids Through Community Care

The United States is battling an opioid overdose epidemic, and Veterans are at almost double the risk compared to the general population. Veterans Health Administration (VHA) recognizes the critical role naloxone plays as a risk mitigation strategy in opioid prescribing; however, there was not a standardized process within the Kansas City VA Medical Center’s (KCVAMC) community care program. This quality improvement project included Veterans that received opioids through community care from 2022 to 2023. The Stratification Tool for Opioid Risk Mitigation and chart review were used along with descriptive statistics. Results found 11% (22 of 206) of Veterans were dispensed naloxone within one year prior to receiving their opioid prescription. This data indicated the importance of expanding access to naloxone in Veterans receiving opioids from community care providers. As a result of this data, the KCVAMC implemented an outpatient pharmacy protocol to dispense naloxone to appropriate Veterans that receive an opioid through the community care program. The protocol was initiated on March 29, 2023. From the end of March through 2023, naloxone dispensing for these Veterans increased from 11% to 67%.