Decision Analysis Research Articles

Advanced financial modeling in healthcare investments: A framework for optimizing sustainability and impact

Healthcare investments play a critical role in improving public health outcomes and fostering economic growth. However, the dual objectives of maximizing financial returns and achieving meaningful social impact present significant challenges. This study proposes a comprehensive framework for applying advanced financial modeling techniques to healthcare investments, with a focus on optimizing sustainability and impact. The framework integrates cutting-edge financial modeling approaches, including predictive analytics, scenario planning, and multi-criteria decision analysis, to address the complexities inherent in the healthcare sector. Risk management techniques are explored to identify, quantify, and mitigate uncertainties that can undermine investment performance. The framework emphasizes the use of advanced analytics to project financial returns while incorporating sensitivity analyses to account for market volatility and policy changes. Additionally, resource allocation models are proposed to optimize the distribution of financial resources across various healthcare initiatives, balancing profitability with social value creation. A critical component of the framework is its emphasis on sustainability. By incorporating environmental, social, and governance (ESG) metrics into financial models, the proposed approach ensures that healthcare investments align with long-term sustainability goals. The framework also includes impact assessment tools to measure and evaluate the social and economic benefits of healthcare investments, ensuring that outcomes align with stakeholders' expectations. The study highlights the importance of a data-driven approach in bridging the gap between financial objectives and social impact. Case studies of successful healthcare investments are used to validate the proposed framework, demonstrating its practical applicability and potential to drive significant improvements in healthcare delivery. In conclusion, this framework provides a robust foundation for healthcare investors to navigate the complexities of the sector, enabling them to achieve a harmonious balance between financial sustainability and impactful outcomes. By leveraging advanced financial modeling techniques, this study contributes to the growing discourse on sustainable healthcare investments and underscores their importance in addressing global health challenges. Keywords: Advanced Financial Modeling, Healthcare Investments, Sustainability, Risk Management, Return Projections, Resource Allocation, ESG Metrics, Social Impact, Data-Driven Approach, Global Health.

Construction and analysis of Aczel-Alsina t-norm based (p,q)-rung linear diophantine fuzzy aggregation operators with multi-criteria decision making

Aggregation operators ( AO s) are employed in various fuzzy environments to accommodate uncertain information. Aczel-Alsina ( AA ) t-norm and t-conorm inherently flexible endows Aczel-Alsina AO s with greater adaptability and robustness in the aggregation process than operators rooted in other t-norm and t-conorm families. Further, ( p , q ) -rung linear Diophantine fuzzy set ( ( p , q ) - RLDFS ) is one of the advanced versions of the fuzzy set ( FS ), which has been revealed to be very successful at combating uncertain information and has grown in prominence in decision analysis. The article aims to establish AA operations laws for ( p , q ) - RLDFS s. Furthermore, we expose the ( p , q ) -rung linear Diophantine fuzzy Aczel-Alsina weighted average ( ( p , q ) -RLDFAAWA), ( p , q ) -rung linear Diophantine fuzzy Aczel-Alsina weighted geometric ( ( p , q ) -RLDFAAWG) operators and systematically analyze their unique features and results with concrete examples. Additionally, based on the devised AO s, a multi-criteria decision making ( MCDM ) approach is designed to choose optimal agricultural technological systems under the ( p , q ) - RLDFS framework. The proposed model is subjected to a comprehensive comparison against various existing studies to demonstrate its superiority in terms of reliability and accuracy. Moreover, the influence of key parameters, designated as Λ, p , and q , on the ranking outcomes is thoroughly examined to assess the applicability and robustness of the results derived from the developed method.

Systematic Development of a Detergent Toolbox as an Alternative to Triton X-100.

Detergents are routinely included in protein purification processes to inactivate enveloped viruses that may arise from adventitious or endogenous contamination. The detergent Triton X-100 (TX-100) has been widely used as part of the production process for therapeutic proteins. However, recent ecological studies indicate that TX-100 and its metabolites detrimentally impact aquatic organisms, thus alternative detergents for viral inactivation are required. The overall aim of this study was to identify one or more detergents that are a suitable replacement for TX-100 in the viral inactivation step. In stage one, 16 potential alternatives were identified and screened against TX-100 using multiple criteria such as solubility, feasibility of virus inactivation, critical micelle concentration, and storage conditions. The multi-criteria decision analysis (MCDA) methodology was used to identify four candidates for the second stage assessment. In stage two, a detailed evaluation was undertaken and two candidates C16-AO, and C11/15-sEO9, were found to be practical alternatives to TX-100 for use in protein therapeutic production processes for inactivating enveloped viruses. In addition, C13-EO8 demonstrated good viral inactivation capability and warrants further investigation in detergent clearance and impact on product quality.

A New Multi-Criteria Decision-Making Tool for Subsea Oil and Gas Asset Decommissioning

Abstract The decommissioning of offshore oil and gas (O&G) facilities poses significant challenges due to high costs, operational risks, social impacts, and environmental protection concerns. Multi-criteria decision analysis (MCDA) emerges as a vital tool to navigate these complexities, enabling stakeholders to evaluate diverse decommissioning alternatives against a broad set of criteria. Existing literature lacks extensive documentation on the application of MCDA in decommissioning decisions, particularly in quantitatively evaluating the performance of different decommissioning strategies against a comprehensive set of criteria. This study introduces a novel MCDA model tailored for the decommissioning of subsea O&G assets, aiming to improve decision-making by providing a quantitative ranking of decommissioning alternatives. Employing the Preference Ranking Organization Method for Enrichment Evaluations method, the model considers 37 attributes across six criteria (safety, environment, waste management, technical, social, and economic) to evaluate several decommissioning alternatives for the Brazilian Espadarte Field. Weightings and scenarios are defined through expert consultation, enhancing the model’s adaptability and relevance to real-world contexts. The model’s application demonstrated significant efficacy, with the selected top-ranked decommissioning alternative achieving a performance indicator of 58%, corresponding to the removal of 50% of the pipeline length considered. This result underscores the model’s capacity to identify optimal compromises between competing criteria. The study contributes to the field by filling the gap in the quantitative evaluation of decommissioning alternatives, offering a robust, transparent decision-making tool that accommodates complex, multi-criteria decisions. The model provides a significant advancement in the rationalization of decommissioning strategies for O&G assets.

Pollution risk assessment in sub-basins of an open dump using drones and geographic information systems.

The sustainable management of municipal solid waste (MSW) presents a pressing global challenge. This study introduces an innovative methodology for analysing open dumps in Tucumán, Argentina, using unmanned aerial vehicles (UAVs) and DroneDeploy software for data collection, coupled with QGIS for estimating contamination risk at the sub-basin level. By integrating satellite imagery, ground surveys, high-resolution UAV imagery and a multi-criteria decision analysis within geographic information system, we provide a comprehensive overview of dumpsite conditions at one open dump. Commercial drone flights facilitate the rapid and cost-effective creation of digital elevation models and digital terrain models, along with orthomosaic imagery, from which waste footprints are delineated using artificial intelligence to enhance the understanding of geospatial issues. Approaching data layers, such as leachate pools, riverbanks and solar radiation, supports informed decision-making in MSW management through a replicable methodology. Field validation and the inclusion of subsurface and groundwater processes are recommended for future research to improve accuracy and maximize socio-ecological benefits.

The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands

Abstract Arid/semi-arid regions face water challenges much like the Arabian Peninsula, which is primarily caused by continuing shortages and growing activities of reclaimed land, as well as industrial and domestic activities. Consequently, identifying groundwater prospective zones (GWPZs) has become essential for securing water resources. The study aims to delineate and predict the best areas of groundwater prospection and abstraction by implementing the analytical hierarchy process-geographic information system (GIS) techniques in a rough terrain that occupies ∼70% of fractured hard rocks including ∼34% of the basaltic flow sheet of Wadi Marawani, Saudi Arabia. To investigate the combined impact of the model, 13 input thematic maps, including elevation, slope, curvature, depression, drainage density, Topographic Wetness Index, distance to river, Stream Power Index, Terrain Roughness Index, geology, lineaments, Normalized Difference Vegetation Index, and rainfall factors, were created, and employed in the model, which was subsequently merged through GIS techniques to reveal prospective zones. These maps are mainly derived from Shuttle Radar Topography Mission, Sentinel-1, Landsat, and Tropical Rainfall Measuring Mission. The output map is categorized as very low, low, moderate, high, or very high, and excellent occupying ∼7%. This promising zone is the result of the intersection of several criteria that control groundwater occurrences. The results were enhanced by implementing optical and radar remote sensing data, and thus, suitable recharge places for the future governance and abstraction of groundwater have been identified using GIS–AHP–multicriteria decision analysis methods. For validation, large numbers of well/spring locations that reached 415 are used in total. The efficiency of the model is estimated at 79.90% (area under curve) based on the receiver operating characteristic curve. Moreover, the Interferometry Synthetic Aperture Radar coherence change detection image validated the predicted model and revealed areas of no-coherence areas marked in brown matched to vegetated areas and excellent zones of GWPZs. The applied methodologies and findings of this study present significant insights for water resources planning and management to develop groundwater resources in similar regions worldwide.

Women Traficking and the Legal Provisions in India

Human trafficking, especially the exploitation of women, continues to be a severe violation of human rights both in India and across the globe. This issue involves deceptive and coercive practices that exploit women, often forcing them into various forms of servitude, including sexual exploitation. Although legal safeguards, such as Article 23 of the Indian Constitution, which bans trafficking, and various national laws intended to combat this issue, exist, human trafficking continues to thrive. Contributing factors include economic vulnerability, inadequate law enforcement, and entrenched social stigmas. This paper investigates the current situation of women's trafficking in India, highlighting important laws like the Immoral Traffic (Prevention) Act (ITPA) and the Suppression of Immoral Traffic in Women and Girls Act (SITA). It also examines the deficiencies in the legal framework, the cycle of exploitation that perpetuates trafficking, and the critical need for stronger and more comprehensive approaches. Through an analysis of research, judicial decisions, and legal measures, this study seeks to offer an in-depth understanding of the issue and propose effective solutions to address the trafficking of women.

Empirical analysis of intelligent charging Decisions: Boosting efficiency for electric trucks

Empirical analysis of intelligent charging Decisions: Boosting efficiency for electric trucks

Composition as Nonlinear Combination in Semantic Space: A Computational Characterization of Compound Processing.

Most Chinese words are compounds formed through the combination of meaningful characters. Yet, due to compositional complexity, it is poorly understood how this combinatorial process affects the access to the whole-word meaning. In the present study, we turned to the recent development in compositional distributional semantics, and employed a deep neural network to learn the less-than-systematic relationship between the constituent characters and the compound words. Based on the compositional representations derived from the computational model, we investigated the combinatorial process in terms of the degree of overlap between the compositional and the lexicalized representations as well as the degree of distinctness of the compositional representation. Analyses of lexical decision and eye-tracking data revealed the effects of both compositional attributes over and above the effects of constituent character features and compound features, indicating an active engagement of the combinatorial process in compound processing. Moreover, with the increase of compound frequency, and thus the increased likelihood that the holistic route prevails, these compositional effects appeared to be dampened. These findings, therefore, provided a computational characterization for the dual-route framework, which sheds light on the universal process of compound comprehension across different languages.

GRA and CoCoSo Based Analysis for Optimal Performance Decisions in Sustainable Grinding Operation

Currently, researchers are continually thinking of intelligent and sustainable manufacturing methods. Surface grinding is the finishing machining process performed for dimensional accuracy and surface smoothness. The heat caused during grinding hinders these responses, leading to poor quality and rejection of the workpiece, which has been produced to its entire value. So, optimizing the surface grinding input parameters controlling the output responses is crucial. This is generally achieved using Taguchi and other optimization methods. In the case of multiple responses, equal weightage is considered for all the responses to get an optimized input parameter setting. This gives less flexibility for the decision-maker to choose the grinding parameters following his priorities for the responses. The issue is addressed with two effective Multi-Attribute Decision-Making (MADM) methods, namely Grey Relational Analysis (GRA) and Combined Compromise Solution (CoCoSo). This paper focuses on applying the above MADM methods for ranking the grinding input parameters settings obtained from the Taguchi analysis of the selected case study, surface grinding of EN8 steel plates using a Cashew Nut Shell Liquid (CNSL), a green Cutting Fluid (CF). Two sets of weights are considered for the dual responses of the selected study to obtain the ranking of the grinding parameters to aid the decision-maker in making flexible decisions. The ranking correlation studies showing high correlation and statistical significance are also presented. Both the GRA and CoCoSo approaches are efficient, relatively simple to comprehend, and provide a flexible strategy for the decision-maker to make intelligent decisions, avoiding trial and error.

Mapping the social-ecological suitability of agroforestry in the US Midwest

Abstract Agroforestry practices offer the potential to increase agricultural sustainability, but their adoption remains limited, especially in some of the world’s most highly productive regions like the United States (US) Midwest. Integrated assessment of the environmental, social, and economic factors that determine agroforestry’s potential benefits and social acceptability is needed to effectively target agroforestry expansion. To meet this need, we used geospatial multi-criteria decision analysis to map agroforestry’s suitability across the US Midwest. We mapped priority areas where agroforestry is expected to reduce the risk of environmental degradation, provide productive tree growth, and be socially and economically viable. We show that integrating social and economic factors dramatically shifted priorities compared to an environmental-only suitability assessment. Using COMET-Planner, we estimated that expanding agroforestry to the top 5% most suitable land from our analysis (totaling 18.3 million acres) has the potential to store 43 [29–58] Mt of CO2e per year over the business-as-usual scenario. Our integrated approach can help stakeholders identify target areas for agroforestry and provides a theoretical foundation for interdisciplinary suitability mapping that can be adapted for use in other global regions.

A Standardized Measurement and Valuation Scale of Genomic Utility for Policy Decisions: The GUV Scale.

The multifaceted ways in which genomics can be valuable to clinicians, patients, families, and society are important for informing prioritization decisions by policy makers. This study aims to develop a standardized, cumulative, and preference-weighted genomic utility valuation (GUV) on a scale of 0% to 100%. A multicriteria decision analysis was conducted with experts involved in policy, clinical, research, and consumer advocacy leadership in Australia for the valuation of policy priority indicators of genomic utility. The use of the GUV scale to support policy decisions is illustrated through a stylized example, and benchmark scoring thresholds of genomic utility were identified by mapping evidence from real-world health technology assessments leading to the public reimbursement of genomic testing in Australia onto the GUV scale. In total, 33 (73%) invited experts participated in the study. Clinical utility had the highest priority, followed by societal, diagnostic, economic, and family utilities. Improving health outcomes had the highest preference value (29.5%), followed by improving equity (22.6%), Having high diagnostic yield (22.2%), improving symptom management (15.5%), being cost saving (14.3%), having average diagnostic yield (13.1%), enabling access to clinical trials (12.3%), and enabling reproductive family planning (11.5%). Genomic testing scores from real-world health technology assessments ranged from 46% for syndromic and nonsyndromic intellectual disability to about 60% for mitochondrial conditions and genetic kidney diseases. Comparisons of genomic utility across different clinical contexts may seem difficult because of the multiple criteria required to be weighted to support policy decisions. This comparison is now facilitated in a standardized manner with the GUV scale.

Integrating adult occurrence and reproduction data to identify conservation measures for amphibians.

Monitoring programs are pivotal to establishing sound management. Due to economic, logistic, and time limitations, monitoring programs often overlook differences among life-history stages. However, species occurrence does not necessarily mean population viability, and it is unclear to what extent monitoring programs that do not consider separately adult presence and reproduction provide effective management indications. Unfortunately, collecting data on certain life stages requires high sampling effort, leading to a trade-off between model reliability and resources needed for monitoring. We collected data on presence and reproduction of amphibians by monitoring 207 waterbodies in Lombardy (northern Italy) in 2017-2022. We then used multistate occupancy models to test whether certain environmental features, namely, pond area, hydroperiod, forest cover, shade, aquatic vegetation, and predators' presence, differentially affected adult occurrence and breeding probabilities of multiple amphibian species. To assess optimal sampling efforts, we modeled the detection probabilities of adults and reproduction across multiple species. Finally, we identified the optimal monitoring strategy under different scenarios of resource availability, comparing adult-only monitoring versus joint assessment of the occurrence of adults and reproduction. In many cases, the main drivers of adult occurrence and reproduction did not coincide because most investigated ecological variables affected one life stage or the other. Forest area, for instance, increased occurrence probabilities of adults of the endemic Rana latastei but showed no effect on their reproduction probabilities. Quantitative estimates of the sampling effort showed that occurrence of adults was easier to spot in 4 out of 7 species. Multicriteria decision analyses showed that when resources were scarce, monitoring adults was the optimal strategy for those 4 species. Conversely, with more resources, monitoring both adults and reproduction emerged as the best strategy for all the considered species. Integrated monitoring of adults and reproduction is essential to comprehensively identify effective conservation measures for amphibians.

Assessment of current and future land sensitivity to degradation under climate change in the upstream Ouergha watershed (Morocco) using GIS and AHP method

Land degradation is a worldwide phenomenon that negatively affects the topsoil of agricultural land. The Mediterranean region is the hardest hit by this phenomenon, which is set to increase due to climate change. This study aims to assess the susceptibility of land to degradation under climate change, based on the calculation of the Land Degradation Sensitivity Index (LDI) in the upstream Ouergha catchment in in Northern Morocco (Southern Mediterranean), using Geographic Information Systems (GIS) and the Analytic hierarchy process (AHP) multi-criteria decision analysis (MCDM) method. In this study, 4 main criteria (climate, vegetation, soil and human environment) divided into 15 sub-criteria were used to assess soil sensitivity to degradation. The weight of each criterion was assigned using the AHP method. The degree of soil degradation was classified on a map based on four categories (low, moderate, high and critical). About 14.9% of land is not very sensitive to degradation, while 37.2% of land is highly sensitive to degradation. Vegetation and climate were identified as the main drivers of land degradation (37% and 28% respectively). Climate scenarios for model EC-Earth3-Veg (CMIP6) indicate a significant drop in precipitation in this region, which accelerates soil degradation. The result represents a planning tool that can help decision-makers to take the necessary measures to ensure sustainable soil management in the face of climate change

Multicriteria Decision Analysis Coupled with Type-2 Fuzzy Bi-Level Programming for Water Resource Optimization: A Case Study of the Jiulong River Basin, China

Multicriteria Decision Analysis Coupled with Type-2 Fuzzy Bi-Level Programming for Water Resource Optimization: A Case Study of the Jiulong River Basin, China

From collective to individual radon risk exposure: An insight into the current European regulation.

Radon (222Rn) is a radioactive gas with well-documented harmful effects; the World Health Organization has confirmed it as a cancerogenic for humans. These detrimental effects have prompted Europe to establish national reference levels to protect the exposed population. This is reflected in European directive 59/2013/EURATOM, which has been transposed into the national regulations of EU Member States. Specifically, the directive requires the identification of Radon Priority Areas to facilitate remediation in regions with high Rn levels. The regulation also includes measures for radiation protection, aiming to safeguard the population collectively and individuals from Rn exposure. These two requirements can be conceptualised and translated into two complementary concepts: collective and individual risk. This work addresses the lack of a standardised methodology at the European level for defining radon (Rn) risk across regions. It provides the first approach to transitioning from collective to individual risk areas (CRAs to IRAs), offering clear insights into the application of European Rn protection regulations. Key challenges have been addressed, including geo-hazard mapping without a response variable, evaluating the performance of Spatial Multi-Criteria Decision Analysis, and assessing the use and representativeness of available indoor Rn data to support individual risk assessment. The study also explores the optimal scale for delineating Radon Priority Areas. The effectiveness of this novel approach, which incorporates both collective and individual risk factors in accordance with European regulations, has been tested in a case study in the Bolzano province (north-eastern Italy).

Neuromorphic computing: A game changer in financial planning and analysis systems

This research examines the role of neuromorphic computing as an innovation that will revolutionize the FP&A systems by enhancing the efficiency at which large and complex data sets are processed. Conducting both quantitative and qualitative analysis, the work outlines the advantages of neuromorphic systems compared with the traditional computing systems based on parameters like processing time, accuracy of forecasting and power consumption. Spectrum highlights: Faster data processing, reduced energy consumption by 70 percent and better sales forecasts. Moreover, experts’ qualitative knowledge that describes experiences from the financial markets reveals the ability of neuromorphic systems when it comes to market volatility. Hurdles include infrastructure integration and cost, but the study also shows that neuromorphic computing holds great promise for transforming the financial industry through providing efficient data analysis for decision making and encouraging sustainable practices. The results presented in the paper open up the discussion about the usability of such a technology in the field of finance and other fields.

Dihydropyrimidine Dehydrogenase Deficiency (DPYD) Genotyping-Guided Fluoropyrimidine-Based Adjuvant Chemotherapy for Breast Cancer. A Cost-Effectiveness Analysis.

While standard doses of adjuvant fluoropyrimidine-based chemotherapies are generally safe for most patients, the risk of severe adverse drug reactions (ADRs) is increased for those with dihydropyrimidine dehydrogenase deficiency (DPYD), a genetic variation that affects drug metabolism. The objective of this study was to examine the cost effectiveness of offering DPYD pharmacogenetic-guided care, where genetic testing informs personalized dosing versus the current standard of care (SoC), which involves administering fluoropyrimidine-based therapies without prior genetic screening, for local or metastatic breast cancer patients in Qatar. We developed a two-stage decision analysis, with an analytic tree model over a 6-month period, followed by a life-table Markov model over a lifetime horizon. We compared the current SoC with the alternate strategy of DPYD genetic screening in patients living in Qatar with local or metastatic breast cancer who were eligible for adjuvant fluoropyrimidine therapy. Clinical outcomes and utilities were obtained from published studies, while healthcare costs were estimated from Hamad Medical Corporation, Qatar. The short-term outcome included the incremental cost-effectiveness ratio (ICER), defined as cost per success (survival without grade III/IV ADRs) at 6 months. The long-term outcome was the ICER, defined as cost per quality-adjusted life year (QALY) gained, with a 3% annual discount rate. The study adopted a public healthcare perspective in Qatar. Sensitivity analyses were conducted to explore the impact of key input parameters on the robustness of the model. In the short-term model, at its base case, DPYD genomic screening was dominant over SoC with a mean cost-saving of QAR84,585 (95% confidence interval [CI], 45,270-151,657). This cost saving reflects the overall economic benefits associated with the implementation of DPYD genomic screening. In the long-term model, compared to the current SoC, DPYD genetic screening would result in an ICER of QAR21,107 (95% CI -59,382-145,664) per QALY gained. Based on our model, implementing DPYD genetic screening to detect DPYD mutations in breast cancer patients before therapy initiation seems to be a cost-saving and cost-effective strategy in Qatar.

A two-stage model of the fuzzy analytic hierarchy process and the fuzzy synthetic evaluation technique to prioritize sustainable sanitation services under uncertainty

In the context of the Sustainable Development Goals (SDGs), which strive to ensure comprehensive access to fundamental water, sanitation, and hygiene (WASH) services, it is extremely imperative to prioritize communities in need and still disadvantaged. Moreover, tackling the worldwide sanitation crisis entails advancing the development of productive and sustainable sanitation systems and infrastructure. Sanitation planning is a multidimensional exercise encompassing multiple dimensions, stakeholders, and strategies, typically with conflicting objectives. Poor planning, funding obstacles, stakeholder priorities, climatic changes, growing populations, system constraints, and user engagement all complicate the entire process. Intelligent strategic decision-making is crucial, particularly for resource-constrained economies. Multi-criteria decision analysis (MCDA) models offer opportunities to figure out and resolve such conflicts, while also optimizing prioritization and policymaking. These models assist with considering trade-offs, data uncertainty, and arriving at decisions by considering technical, economic, social, and environmental sustainability. In the present work, a two-stage integrated model of the Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Synthetic Evaluation Technique (FSET) was developed, accompanied by a sensitivity analysis, yielding an assessment index designated as the Sanitation Priority Index (SPI). This index is particularly applicable in prioritizing and categorizing communities in need of sanitation infrastructure (e.g., wastewater collection systems and treatment plants), considering competition over scarce resources, reliance on third-party funds, and sustainability factors. The decision-making problem was designed as a hierarchical structure that integrates all problem elements and specifies the key criteria contributing to the SPI. Fuzzy set theory handles data uncertainty, with FAHP evaluating criteria significance in a group decision-making context and FSET designing criteria membership functions. Field data on criteria contributing to the SPI is transformed into fuzzy intervals, synthesized, and defuzzified to derive the SPI at community level. The model’s robustness is examined using sensitivity analysis as it is applied to several Palestinian communities lacking sanitation infrastructure. The outcomes show that the demographic criterion has the major impact on the SPI (20.38%), followed by water consumption (16.76%) and wastewater reuse potential (15.40%). Environmental risks account for 12.40%, utilities’ competency (11.5%), and industrial wastes risks (8.72%). The socioeconomic context is valued at 5.10%, geographical constraints at 4.51%, and license constraints at 4.8%. Out of the 25 communities investigated, five exhibited SPI values surpassing 60.0%. Eleven communities possessed SPI values between 50.0% and 60.0%, and nine achieved SPI values ranging from 40.0 to 50.0%. The sensitivity analysis application reveals almost complete stability in prioritizing communities. Introducing this model into relevant bodies’ sanitation management practices and planning strategies holds the potential to significantly boost sustainable sanitation services as well as the performance of water and wastewater utilities. It further enables the incorporation of additional criteria and the interests of more stakeholders.

Nuclear leak spatial risk assessment with analytical hierarchy process (AHP) from multi-criteria decision analysis (MCDA) methods (case study of Türkiye Nuclear Power Plants)

Nuclear leak spatial risk assessment with analytical hierarchy process (AHP) from multi-criteria decision analysis (MCDA) methods (case study of Türkiye Nuclear Power Plants)