Multi-criteria Decision Analysis Tool Research Articles

Current and emerging waste-to-energy technologies: A comparative study with multi-criteria decision analysis

In response to the rise in waste crisis and the possibility of energy utilization from waste, there has been increasing interest in waste-to-energy (WtE) conversion technologies, which requires intense scientific attention. There are diverse WtE technologies that apply to different waste types and require multidisciplinary decision support. The paper applies a Multi-criteria Decision Analysis (MCDA) tool to compare their economic, technological, socio-cultural, and environmental aspects to help identify the most promising choice. The comparison used in this study concerns four widely used technologies: Incineration (INC), Anaerobic Digestion (AD), Gasification (GAS), and Pyrolysis (PYR), and one emerging WtE conversion technology, Hydro-thermal Carbonization (HTC). The Comparison criteria are divided into four main criteria and fifteen sub-criteria. The Analytical Hierarchy Process (AHP) model was implemented using ’SuperDecisions’ software to make pairwise comparisons of identified criteria and to rank the WtE technology alternatives. Thirty-two international studies were shortlisted to gather data and provide input into the AHP model. The results show that the environmental factors are prioritized with a priority vector of 0.56. Further, the study concludes that the most suitable WtE technology, based on chosen parameters, is AD, followed by HTC, INC, and PYR with the priority vectors of 0.348, 0.201, 0.162, and 0.148, respectively, provided applicability. The emerging technology, HTC, is found to be the second most suitable technology. Further, the results represent the hierarchy structure arranged so that the main components are divided into sub-components with alternatives at the structure’s base, and the ’SuperDecisions’ model based on this hierarchy can be used in the future to find suitable WtE technology for a specific city with the necessary input for identified main and sub-criteria. This research not only provides a structured comparison of WtE technologies but also offers a scalable AHP framework that can be adapted for specific municipal contexts in future studies. By addressing the diverse needs of decision-makers across different regions, our model contributes to a more nuanced understanding of WtE technology selection and lays the groundwork for incorporating local policies and regulations in subsequent research phases.

SITW Method: A New Approach to Re-identifying Multi-criteria Weights in Complex Decision Analysis

Multi-Criteria Decision Analysis (MCDA) addresses complex decision-making problems across various fields such as logistics, management, medicine, and sustainability. MCDA tools provide a structured approach to evaluating decisions with multiple conflicting criteria, assisting decision-makers in navigating intricate scenarios. Engaging experts is crucial for identifying multi-criteria models due to the diverse aspects of decision-making problems. Techniques such as pairwise comparisons and criterion weight assignment are commonly used to incorporate expert knowledge into decision models. Criterion weight assignment allows experts to indicate the importance of each criterion; however, issues can arise if model parameters are lost or experts become unavailable. To mitigate these issues, techniques like entropy or standard deviation can determine weights without direct expert input. In this context, the Stochastic Identification of Weights (SITW) method utilizes existing assessment samples to re-identify models and obtain weights that replicate the rankings of a reference model. This study compares information-based methods (Entropy, STD) with the SITW method in re-identifying the TRI medical function as a benchmark. The effectiveness of these methods is evaluated using Spearman's weighted correlation coefficient across various scenarios and alternative numbers. Results indicate that the SITW method provides more significant results than other methods in identifying multi-criteria weights by leveraging previously evaluated alternatives. Future research could explore broader approaches and uncertainty scenarios to ensure comprehensive decision support in complex contexts.

Advanced Decision- Making Framework for Sustainable Energy Retrofit of Existing Commercial Office Buildings

In the background of rising global initiatives to combat climate change and promote better energy management, retrofitting of current structures has become a major strategy. This abstract and literature review aims at presenting a full review of a decision-making approach that can be used in choosing sustainable options for energy retrofitting. This particular framework is designed to help guide retrofit decision-making due to the many issues that surround the process by providing an economic as well as an environmental and social context. Energy retrofitting means improving the existing structure or complex to decrease its energy intensity and have a negative impact on the environment. As stated earlier, it is a critical process for attaining sustainability goals and advancing the performance of buildings. But the decision on which retrofit measures should be implemented can be rather difficult because of the availability of a vast number of technologies and because more factors must be considered, such as cost and energy savings. The decision-making framework provided in this article aims at categorizing retrofit selection into this list of features and views the current article as a way of making the process more efficient. It encompasses the life-cycle cost analysis (LCCA), the economic valuation, and the multi-criteria decision analysis tools and criteria. These tools facilitate the evaluation of financial viability, the advantages of climatically retrofitting, and the environmental effects of distinct retrofitting choices. The above-mentioned framework is exemplified with a case study of an office building retrofit project. The following case includes an example of the application of the mentioned framework and illustrates the explanation of how it helps to make decisions. Due to the consideration of costs, energy, and environmental performance, the framework enables the stakeholders to choose appropriate retrofit measures. Other than the case study, the framework also focuses on the need to establish a sustainable energy retrofit DSS. Thus, the DSS supplements collected data on building performance, retrofit technologies, and economic aspects, which can be accessed by the stakeholders in real-time and with built-in facilities for scenario analysis. This system improves decision-making since retrofit results can be monitored and evaluated frequently. Both tools and strategies for the decision-making of sustainable energy retrofits bring a logical and systematic approach aimed at neutralizing the decision-making challenges related to retrofit selection. By applying economic, environmental, and social factors in the decision-making process, the framework assists the stakeholders in reaching energy efficiency and sustainability goals. The development of another strong decision-support system also improves the working of the framework and checks that retrofit projects are done efficiently and are strategic to the general goal of sustainable development.

Strategic assessment of groundwater potential zones: a hybrid geospatial approach

Groundwater aquifers constitute the primary water supply for populations in arid regions, exemplified by the Goharkooh Plain in Iran's driest drainage basin, where conditions of high evapotranspiration and low precipitation prevail. With the escalating demand for water resources, driven mainly by agricultural expansion, the strategic management of groundwater assets has become increasingly critical. This study focuses on delineating groundwater potential zones (GWPZs) through an integrated approach combining multi-criteria decision analysis and geospatial tools. Based on an extensive literature review, nine thematic layers were selected and developed: lithology, geology, drainage density, slope gradient, elevation, vegetation cover, lineament density, land use, and precipitation. These criteria were initially weighted using the analytical hierarchical process (AHP) and subsequently integrated via weighted overlay analysis. In this research, the strategic selection of thematic layers for assessing groundwater potential in arid regions has been identified as an innovative approach that could significantly advance studies in similar settings. The analysis revealed that approximately 60% of the study area, primarily in the southwestern parts, exhibited moderate to very high groundwater potential. This potential is primarily attributed to the presence of alluvial deposits, low drainage density, and favorable slope and elevation conditions. Applying the receiver operating characteristic (ROC) curve yields an area under the curve (AUC) of 81.5%, indicating a relatively high level of predictive accuracy. These findings demonstrate the efficacy of this integrated approach, suggesting its broader applicability in regions with analogous groundwater challenges and management needs.

Perbandingan Efisiensi Bahan Bakar Pada Kendaraan Low MPV Dengan Metode Copras

Low multi-purpose vehicles have become a popular choice in the automotive market due to their combination of ample space and improved fuel efficiency. In the context of increasing environmental awareness and ever-increasing fuel costs, this study aims to compare the fuel efficiency of various low multi-purpose vehicles using the Complex Proportional Assessment (COPRAS) method. The Copras method, as a multi-criteria decision analysis tool, is used to evaluate vehicle alternatives based on a number of predefined criteria. In this study, the main criteria considered include engine efficiency, aerodynamics, vehicle weight, and fuel technology used. The identification of these criteria is important as fuel efficiency is influenced by various complex technical factors. Relevant data was collected for the low multi-purpose vehicles to be evaluated. Once the data was collected, a Copras analysis was conducted to determine the relative ranking of each vehicle based on its fuel efficiency. This method allowed the researcher to identify vehicles that stood out in terms of fuel efficiency. The results of this study are expected to provide valuable insights for both consumers and vehicle manufacturers. Consumers can use this information to make smarter decisions when choosing a vehicle that suits their needs, taking into account the important aspect of fuel efficiency. On the other hand, manufacturers can use these findings to develop more fuel-efficient vehicles, as well as to strengthen their position in an increasingly competitive market. In addition, this study also has significant implications in an environmental context. By choosing more fuel-efficient vehicles, consumers can contribute to the reduction of greenhouse gas emissions and reduce other negative impacts on the environment. As such, this research not only provides economic benefits but also supports efforts to maintain environmental sustainability

Selection of unmanned aerial vehicle systems for border monitoring using the MPSI-SPOTIS method

PurposeThe present paper assesses the decision problem of selecting Unmanned Aerial Vehicle Systems (SARP) by the hybrid MPSI-SPOTIS approach for deployment in border control and transborder illicit combat.Design/methodology/approachBy the hybrid MCDA MPSI-SPOTIS approach, and from the database available in Gettinger (2019), models were filtered by Endurance, Range, Maximum Take-Off Weight (MTOW), and Payload, fitting within the classification of Categories EB 0 and 2. Category EB 1 was not considered in this study due to the limited number of models in the data source.FindingsThe use of the Multi-Criteria Decision Analysis (MCDA) tool MPSI-SPOTIS allowed the determination of weights by stochastic criteria, applied in a ranking method resistant to reverse ordering. The application of the method identified the Raybird-3 (Cat EB 0) and Searcher (Mk3) (Cat EB 2) models as the best alternatives. From a proposed clustering, other selection possibilities with close performance in the evaluation were presented. The cost criterion was not taken into consideration due to the absence of information in the data source employed. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.Research limitations/implicationsThe cost criterion was not taken into consideration due to the absence of information in the data source used. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models.Originality/valueThis paper aims to propose a technology selection method applied to complex defense acquisitions when multiple factors influence the decision makers and it is hard to obtain a major optimum solution in multitask and multi-mission platform.

Setting a shared development agenda: prioritizing the sustainable development goals in the Dominican Republic with fuzzy-LMAW.

The sustainable development goals (SDGs) were established by the United Nations as an international call to eradicate poverty, safeguard the environment, and guarantee that everyone lives in peace and prosperity by 2030. The SDGs aim to balance growth and sustainability in three dimensions: social, economic and environmental. However, in the post-pandemic era, when resources for public development policies are scarce, nations face the problem of prioritizing which SDGs to pursue. A lack of agreement is one of the determinants of low performance levels of the SDGs, and multicriteria decision analysis tools can help in this task, which is especially relevant in developing countries that are falling behind in achieving the SDGs. To test the feasibility and appropriateness of one of these tools, the Fuzzy Logarithm Methodology of Additive Weights, we apply it to prioritize the SDGs in the Dominican Republic, to see if the priorities established are consistent. Seventeen experts were surveyed, and the main result was that Decent work and economic growth was the most important goal for the country. Our findings, consistent with the literature, show the path to similar applications in other developing countries to enhance performance levels in the achievement of the SDGs.

A framework for evaluation of construction and demolition waste management alternatives using MCDA techniques in the context of Dhaka City

Landfilling and dumping are the common strategies to manage generated construction and demolition waste (C&DW) in Dhaka City. These strategies of C&DW management (C&DWM) affect the environment adversely and fill the old landfill sites quickly. Besides, there isn’t any framework to manage C&DWs effectively. This study introduces a Multi-criteria Decision Analysis (MCDA)-based C&DWM framework for Dhaka City by incorporating the expanded Waste Management Hierarchy (E-WMH). The E-WMH principles are considered as the C&DWM alternatives. Through a comprehensive literature review, the criteria are determined and grouped into social, economic, environmental, and technical criteria to achieve sustainability and effectiveness. To construct the initial decision matrix, the ordinal data (i.e. rating) is collected through interviews with the subject-matter experts of Dhaka City. Also, the ranking system method is used to determine the weight of the criteria. Finally, the ELECTRE I and ELECTRE II MCDA methods are employed to rank the alternatives. Results show that reuse and recovery are suitable strategies for managing C&DWs in Dhaka city. Future studies may consider the hybridization of MCDA tools, quantitative data to form the initial decision matrix, and other mathematical models to weigh the criteria so that the performance of the framework can be enhanced.

Development of the Emirates Multi-Criteria Decision Analysis Tool for Orphan Drugs.

Background The number of orphan drug approvals is currently increasing globally. This creates a significant burden on payers and healthcare systems. This study aimed to create a multi-criteria decision analysis (MCDA) tool for evaluating orphan drugs within the United Arab Emirates (UAE). The intended result of the tool is to provide evidence-based guidance to decision-makers in reimbursement and procurement decisions. Methods We conducted a literature search and local expert interviews to identify relevant preliminary criteria for the MCDA tool. Then we conducted a structured consensus-building session for healthcare experts and decision-makers in the UAE to develop the Emirati MCDA tool for orphan drugs. The experts voted for the criteria to be included in the tool and their ranking according to importance, as well as the weight of each criterion and its scoring function. To improve understanding and facilitate the voting process, experts were provided with a brief illustration of similar tools conducted in other countries before the voting sessions. Finally, the tool was developed in a Microsoft Excel sheet (Microsoft Corporation, Redmond, Washington, United States), and it was validated and tested based on real case studies, then it was fine-tuned accordingly based on the experts' discussions. The final tool was provided to the attendees to guide their decisions in the reimbursement and procurement of orphan drugs. Results The created tool provides a score for each analyzed orphan drug based on its value. Ten criteria were included in the final MCDA tool. These were cost-effectiveness (25.1% of the weight), magnitude of health gain (20.1%), availability of therapeutic alternative (14.3%), disease severity (11%), budget impact (7.9%), disease rarity (5.6%), strength of clinical evidence (5.6%), burden on households (4.5%), indication uniqueness (3.2%), and patients' age (2.6%). Conclusions Implementation of evidence-based healthcare necessitates assessing the fair value of each health technology. Addressing the high unmet medical needs and improving healthcare for patients with rare diseases are priorities within the UAE. The created Emirates MCDA tool for orphan drugs has the potential to help decision-makers implement value-based and evidence-based reimbursement decisions for orphan drugs.

Multicriteria decision analysis tool for faculty performance evaluation in higher education institutions

Higher education institutions (HEIs), which are the major economic drivers in a nation, rely on their faculty for quality. The quality of faculty performance is assessed through performance appraisal methods. Performance appraisal methods help management assess the performance of employees and further the scope of improvement. Traditional methods of performance evaluation based on quantitative data involve more mathematical calculations than scientific approaches. The drawback of these methods is that they consider overall scores and not the individual factors of performance. This is exceptionally relevant when there are multiple disciplines in HEIs. In this study, the Multi-Criteria Decision Analysis (MCDA) model using MACBETH is used to explore its usefulness in explicitly dealing with the trade-offs between performance measures, making the process of performance measurement and management more transparent and defendable. Faculty performance across different disciplines was considered in this study, and faculty performance was evaluated using the MACBETH score. The advantage of MACBETH is that weight can be assigned to specific criteria, and accordingly, analysis can be carried out. Based on which criteria are assigned the highest weight, that factor is considered for the final output. In this study, the results obtained showed significant differences in the performance rankings of the faculty members when compared to those of the traditional methods, suggesting that the FSTs are useful tools for performing performance appraisals.

A system dynamics model and analytical hierarchy process: an integrated approach for achieving sustainable solid waste management system.

Waste management in low-income countries faces challenges with an average cost of $35/ton approximately 51% collection efficiency. Despite investments in treatment, processing, and recycling, the system remains unsustainable owing to poor planning and policies. The current analysis of Lahore's solid waste management (SWM) system, selected as a major city of a low-income country as a case study, focuses on collection efficiency and waste generation. However, it neglects the complex and dynamic nature of SWM systems. To capture the complexities and dynamic nature of the SWM system, system dynamic (SD) modeling is proposed for its effectiveness in modeling complex and dynamic systems. Unlike previous attempts at SD modeling that mostly consider only some components of the SWM system with varying success, this study attempts to use a holistic approach by considering all aspects of an integrated SWM system. In addition, this study explores different financial and management policies, highlighting the weaknesses of the system through a quantitative comparison of three scenarios: (1) business-as-usual (BAU) which considers the current trends in waste generation and practices of collection and disposal to landfill, (2) waste treatment system (WTS) in which various waste treatment systems are included to reduce burden on landfill, and (3) introduction of user fee with awareness campaigns (UFAC) which encourages community participation towards reduction in waste generation and financially supports the SWM. All three scenarios use four indices: waste generation, waste ending up in landfill, uncollected waste, and annual budget deficit as performance indices. These scenarios were simulated over a 25-year period using an SD model, covering all six components of the SWM system. The BAU scenario shows a 16% increase in waste generation, a 173% increase in landfill waste, an 11% reduction in uncollected waste, and a 64% increase in the budget deficit over the simulation period, indicating an unsustainable SWM system. The WTS scenario exhibits a 16% increase in waste generation, a 155% increase in landfill waste, an 11% reduction in uncollected waste, and a 61% increase in the budget deficit, showing a significant reduction in landfill waste and a slight reduction in deficit but it remains unsustainable. The UFAC scenario, however, results in a 40% reduction in waste generation, a 67% decrease in uncollected waste, an 8% decrease in landfill waste, and a 59% decrease in the budget deficit. These results demonstrate that instituting user fees for SWM services and incentivizing community participation towards waste reduction and segregation can make the SWM system of Lahore sustainable. This SD model provides insights for policymakers, aiding what-if analyses and long/short-term waste management plans for metropolitan cities in low-income countries. To validate the sustainability judgments based on performance indices, the analytical hierarchy process (AHP), a multi-criteria decision analysis (MCDA) tool commonly used for ranking policy decisions based on competing criteria, was employed. It considered the same four criteria as in the SD model. The results of the AHP analysis aligned with those of the SD model, ranking the UFAC scenario as the most sustainable option.

HPR181 Comprehensive and Standardized Value Assessment of Innovative Cancer Treatments: A Multi-Criteria Decision Analysis (MCDA) Tool for Switzerland

HPR181 Comprehensive and Standardized Value Assessment of Innovative Cancer Treatments: A Multi-Criteria Decision Analysis (MCDA) Tool for Switzerland

Identification and evaluation of most promising CO2 utilization technologies: Multi criteria decision analysis and techno-economic assessment

Utilization of captured CO2 as a feedstock for the synthesis of valuable products offers an option for supporting climate change objectives and enabling a circular economy. Therefore, there is an increasing attention from governments and industry for promoting the further development of CO2-based applications. Given the multitude of CO2 utilization pathways that are far from equal, a comprehensive assessment is required to identify the most promising options for further development. This study aims to develop a consistent approach for comparing CO2 utilization pathways in terms of multiple criteria and scenarios. To this end, 133 utilization pathways were identified from the literature and evaluated using a Multi Criteria Decision Analysis tool, which is based on an Analytic Hierarchy Process, for three different scenarios. These analyses incorporated technology readiness level, global market size, global market price, CO2 utilization volume, and CO2 retention time as screening criteria. Also, three different scenarios, including Economic Vision, Environmental Vision, and Immediate Environmental Action, were considered to make a decision that reflects the preferences of different decision makers. The results show that calcium carbonate, ethylene, ethylene oxide, methane, polyethylene, polypropylene, syngas, synthetic fuel, and urea are the most promising CO2-based products with respect to the three considered scenarios. As a last step, a general framework was developed to calculate the minimum selling price of the selected CO2 utilization pathways to provide comparable economic results. The highest minimum selling prices were associated with polyethylene and ethylene oxide, where CO2 is incorporated in the respective production process indirectly through another reactant and the highest amount of hydrogen is required.

A critical review of multicriteria decision analysis practices in planning of urban green spaces and nature-based solutions

Abstract Green spaces and nature-based solutions (NBS) are increasingly considered by land-use planning policies to respond to the multiple challenges related to sustainable development. The multiple benefits brought by NBS make the use of multicriteria decision analysis (MCDA) essential to optimally balance their use. MCDA offers a catalog of methods allowing to structure problems with multiple objectives and to help adopt the optimal solution. However, NBS planning is a recent discipline and research is still ongoing to make this practice more common. We carried out a critical literature review on MCDA-NBS tools and practices, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method on the Web of Science database. We selected 124 papers on the subject between 2000 and 2022. We present a state-of-the-art MCDA approach for NBS and green space planning by looking at where these practices are applied, why and how this process is conducted, and who is involved in it. We found that studies are usually conducted in the global North on a single case study with the help of experts involved in the criteria weighting phase and the help of GIS MCDA tools often integrating a direct ranking method or the AHP method.

Assessing neuro-oncology clinical trial impact and value: Testing a novel multi-criteria decision analysis app

BackgroundMany clinical trials are conducted globally, creating challenges in deciding which trial outcomes deserve a clinician’s focus and where to direct limited resources. Determining the ‘value’ of a clinical trial relative to others could be useful in this context. The aim of this study was to test a novel web-based application using multi-criteria decision analysis (MCDA) to rank clinical trial value. MethodsThe MCDA tool combines seven metrics: unmet need; target population size; access; outcomes; cost; academic impact and use of results. Clinical trials were ranked according to their calculated ‘value’ – meaning the importance or worth of a trial. We determined face validity of the app using a set of ten published Phase 3 neuro-oncology clinical trials. A survey of neuro-oncology clinicians asked them to rank the same ten clinical trials, and to rank the seven metrics in terms of importance. ResultsThe two highest app-ranked trials were in concordance with that of the survey respondents, and consistent with the two studies that have had the most impact on routine clinical practice in neuro-oncology. Of the seven metrics, surveyed clinicians considered patient outcomes and unmet need to be the most important when determining clinical trial value. ConclusionsThe metrics app was able to rank and produce a numerical ‘value’ for existing phase 3 neuro-oncology clinical trials. In the future, a related app to prospectively rank future trials at the startup stage could be developed to help centers determine which should be prioritized to be conducted at their site.

Optimal treatment technology selection for acid mine drainage via spherical fuzzy analytic hierarchy process

The Philippines is endowed with large deposits of mineral resources, but mining operations have the potential to negatively impact the environment. For example, acid mine drainage (AMD) is a problem that occurs when sulfide minerals are exposed to air, water, and microbial activity, releasing hazardous elements. Many treatment options have been proposed to date to address these negative impacts of AMD. However, little effort has been made over the years to develop a generalized framework for the selection of the optimum technology for AMD treatment. This study thus proposes the spherical fuzzy analytic hierarchy process (SFAHP) as a multi-criteria decision analysis tool to select the best treatment technology for the treatment of AMD. The use of SFAHP is novel in this context as it addresses uncertainty due to indeterminacy by incorporating a “hesitancy parameter” in the decision-making process. Three treatment options were evaluated using a set of criteria such as technical, economical, and environmental aspects. The results showed that the combination of the two-step neutralization ferrite formation and sulfidogenic bioreactor (TSNFF + SB) was considered the most sustainable, with the environmental aspect being the most important factor. The second most preferred option is the high-density sludge (HDS) process while the least preferred is the combination of the conventional lime neutralization and sulfidogenic bioreactor (CLN + SB). A sensitivity analysis was also done, and a comparison with traditional AHP was conducted, which also found TSNFF + SB to be the best option. This illustrative case study demonstrates the robust framework built from SFAHP for selecting the best technology to manage AMD and addresses uncertainty in the decision-making process.

Multi-Criteria Decision Analyses for the Selection of Hydrological Flood Routing Models

In this study, a framework to circumvent the difficulties in selecting a proper flood routing method was established by employing two different multi-criteria decision analysis (MCDA) tools, namely, TOPSIS and PROMETHEE, with definite decisive criteria such as the error metrics, the number of model parameters, and the model background, under three scenarios. For eight distinct flood datasets, the parameters of 10 different Muskingum models were determined using the water cycle optimization algorithm (WCOA) and the performance of each model was ranked by both MCDA tools considering the hydrograph types of flood datasets, labeled as smooth single peak, non-smooth single peak, multi-peak, and irregular. The results indicate that both tools were compatible by giving similar model results in the rankings of almost all scenarios that include different weights in the criteria. The ranking results from both tools also showed that the routing application in single-peak hydrographs was examined better with empirical models that have a high number of parameters; however, complex hydrographs that have more than one peak with irregular limps can be assessed better using the physical-based routing model that has fewer parameters. The proposed approach serves as an extensive analysis in finding a good agreement between measured and routed hydrographs for flood modelers about the estimation capabilities of commonly used Muskingum models considering the importance of correlation, model complexity, and hydrograph characteristics.

Prioritization of vegetable-borne biological hazards in Argentina using a multicriteria decision analysis tool.

Vegetables, especially those eaten raw, have been implicated in several foodborne disease outbreaks. Since multiple vegetable matrices and hazards are involved, risk managers have to prioritize those with the greatest impact on public health to design control strategies. In this study, a scientific-based risk ranking of foodborne pathogens transmitted by leafy green vegetables in Argentina was performed. The prioritization process included hazard identification, evaluation criteria identification and definition, criteria weighting, expert survey design and selection and call for experts, hazard score calculation, hazard ranking and variation coefficient, and result analysis. Regression tree analysis determined four risk clusters: high (Cryptosporidum spp., Toxoplasma gondii, Norovirus), moderate (Giardia spp., Listeria spp., Shigella sonnei), low (Shiga toxin-producing Escherichia coli, Ascaris spp., Entamoeba histolytica, Salmonella spp., Rotavirus, Enterovirus) and very low (Campylobacter jejuni, hepatitis A virus and Yersinia pseudotuberculosis). Diseases caused by Norovirus, Cryptosporidium spp. and T. gondii do not require mandatory notification. Neither viruses nor parasites are included as microbiological criteria for foodstuff. The lack of outbreak studies did not allow to accurately identify vegetables as a source of Norovirus disease. Information on listeriosis cases or outbreaks due to vegetable consumption was not available. Shigella spp. was the main responsible for bacterial diarrhea, but it has not been epidemiologically associated with vegetable consumption. The quality of the available information for all hazards studied was very low and low. The implementation of good practice guidelines throughout the entire vegetable production chain could prevent the presence of the identified hazards. The current study allowed the identification of vacancy areas and could help reinforce the need for performing epidemiological studies on foodborne diseases potentially associated with vegetable consumption in Argentina.

Selecting suitable sites for groundwater recharge in Jordan using the spreading techniques via the integration of multi-criteria decision analysis and geographic information system tools

Over exploitation of groundwater resources in Jordan has resulted in the depletion and degradation of many groundwater aquifers. Most aquifers are heavily exploited and are expected to dry up by 2040. These pressing challenges require Jordan to find creative and sustainable solutions. Since the annual natural groundwater recharge rate is limited to 0.1–5%, managed aquifer recharge is a proposed solution for Jordan to enhance recharge. The spreading technique is one method used to deliberately recharge water into groundwater aquifers for subsequent recovery or environmental benefits. This study presents a tool based on a geographic information system-multi criteria analysis to identify the potential sites for groundwater recharge in Jordan using the spreading technique. Large sets of spatial data were used to develop a potential site suitability map for managed aquifer recharge spreading methods; the criteria were chosen based on technical, economic, and environmental considerations. The site assessment combined fourteen thematic maps to create suitability maps using the weighted linear combination approach, which were combined with constraint mapping using the Boolean logic. Constraint mapping showed that nearly 47% (7198 km2) of the study area did not meet the minimum criteria. The site assessment criteria on the remaining areas showed that about 4% (618.7 km2) of the study area has low suitability, 14% (2123 km2) has moderate suitability, 20% (3101 km2) is suitable, and 15% (2388 km2) is highly suitable. Managed aquifer recharge has a great potential in the relief of water stress in arid areas. Existing geospatial tools and the increasing availability of spatial data are a great asset to local authorities in water planners.

Evaluation of Sourcing Decision for Hydrogen Supply Chain Using an Integrated Multi-Criteria Decision Analysis (MCDA) Tool

The use of fossil fuels has caused many environmental issues, including greenhouse gas emissions and associated climate change. Several studies have focused on mitigating this problem. One dynamic direction for emerging sources of future renewable energy is the use of hydrogen energy. In this research, we evaluate the sourcing decision for a hydrogen supply chain in the context of a case study in Thailand using group decision making analysis for policy implications. We use an integrative multi-criteria decision analysis (MCDA) tool which includes an analytic hierarchy process (AHP), fuzzy AHP (FAHP), and data envelopment analysis (DEA) to analyze weighted criteria and sourcing alternatives using data collected from a group of selected experts. A list of criteria related to sustainability paradigms and sourcing decisions for possible use of hydrogen energy, including natural gas, coal, biomass, and water, are evaluated. Our results reveal that political acceptance is considered the most important criterion with a global weight of 0.514 in the context of Thailand. Additionally, natural gas is found to be the foreseeable source for hydrogen production in Thailand with a global weight of 0.313. We also note that the analysis is based on specific data inputs and that an alternative with a lower score does not imply that the source is not worth exploring.