Prioritization Method Research Articles

Governing a union's external stakeholders: A prioritization method based on relationship quality and perceived impact

AbstractStakeholder governance is a growing issue in the literature and for organizations. The complex and uncertain environment of nonprofit organizations forces them to prioritize stakeholder requirements. The traditional criterion of influence and power is nevertheless proving insufficient for these organizations, especially those in political or interest representation sectors. This article proposes to use the relationship quality with stakeholders and its components (trust, satisfaction, influence, engagement) as criteria for prioritization. The study is carried out within the “Young Farmers” union, a French 50,000‐member union defending the interests of agriculture and of farmers under 35 years of age. It questions external stakeholders on the relationship with the organization and its impact and it enables to verify the relevance of these criteria. The results suggest integrating stakeholder emotions into the same‐named theory and studying relationship quality as a process in which satisfaction builds trust that enables engagement, which itself has a positive role on influence. The findings also revisit strategic models of stakeholder prioritization by proposing the four components of relationship quality mentioned above as prioritization attributes. Finally, NPOs’ partners are invited to take into account the subjectivity of the evaluation process and thus to favor reasonable methods to avoid handicapping NPOs.

Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells

There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.

Algorithms for Making Managerial Decisions in the Digital Economy

For solving the existing problems such as loss of sales and customer dissatisfaction, it is necessary to improve promptly the properties of manufactured products in accordance with the dynamically changing market demands. This requires the development of special methods and models for making managerial decisions in conditions of uncertainty and risk. Such decisions could become an algorithm for artificial intelligence of digital technologies, which determines the relevance of the study. The subject of the study is choosing the most significant decisions in conditions of uncertainty and risk. The goal is to find opportunities for making informed decisions for poorly structured, non-formalized processes when developing new product designs with characteristics that meet the rapidly changing needs of the business environment. The solution uses the method of prioritization with expert assessments, groupings, comparisons. The result of the research is the development of a priority setting model with the identification of existing shortcomings and the proposal of changes and additions that eliminate shortcomings in relation to the problem being solved. The author concluded: the developed model, when used in management decisions, allows us to determine the best functions of the product for their inclusion in the design of the innovative model; to make a rating of the significance of functional properties for the consumer and the manufacturer. The development of a methodology with the elimination of the shortcomings of previous studies is a scientific novelty. The obtained methodology contributes to maximizing the demand and competitiveness of the management model, operational innovative changes in the properties of the product corresponding to the rapidly changing demands of the competitive business environment and can be used in the formation of a knowledge base in neural networks of digital technologies. It solves the problem of responding to dynamic changes in consumer preferences, as well as introducing technological innovations in the production of goods, that entail changes in the company’s business processes focused on improving the quality of the final product, which determines the success of strategic business development. The companies’ management may apply the results of the research in the development of corporate governance strategies, researchers, university students, etc.

Prioritization of natural habitats: A methodological framework applied to the French Mediterranean

Long-term preservation of habitats has become a cornerstone of modern conservation policies. As resources allocated to conservation actions are often limited, developing relevant prioritizing methods is necessary. Although many studies have been published on species prioritization, habitats have been the subject of less research. This study aims to develop a simple prioritization method suitable for habitats and appropriate to any typology. We analyzed literature to select criteria that would be the most accurate to rank habitats. Our final method consists in calculating a score based on four criteria: legal obligation, territorial responsibility, conservation condition and an extra criterion designed to fit local interests and objectives. The method is applied on habitats listed in Annex I of the Habitats Directive (92/43/EEC) on the territory of RESEDA-Flore, a network of stakeholders involved in the conservation of Mediterranean flora. Results highlight that dune habitats show the highest conservation values, while rocky habitats and caves obtain relatively low scores. At the top of the ranking, Mediterranean temporary ponds (3170), Dunes with Pinus pinea and/or Pinus pinaster forests (2270) and Coastal dunes with Juniperus spp. (2250) appear to be a high priority. These results can be used to design and implement habitat conservation strategies in the French Mediterranean.

Ranking of Suitable Areas for Establishing Industries in Kashan City Using VIKOR and TOPSIS Methods in Fuzzy Environment

Background: In this research, among the Multi Criteria Decision Making (MCDM) methods, a combination of VIKOR and TOPSIS methods with fuzzy set theory was used to overcome the uncertainty in the ranking of alternatives. The purpose of this paper was to locate the industries of Kashan City and rank the appropriate alternatives using analytic hierarchical process (AHP) and the prioritization methods of fuzzy VIKOR and fuzzy TOPSIS. Methods: In this study, first, the effective criteria in GIS (Geographic Information System) were examined and standardized according to fuzzy logic. Then, by presenting expert opinion through AHP, the criteria were weighted, and in order to determine suitable places for establishing industries, according to the required minimum area, six alternatives were extracted from the weighted linear combination method. Subsequently, the alternatives were ranked using fuzzy VIKOR and fuzzy TOPSIS methods. The sensitivity analysis was performed in the fuzzy TOPSIS method by shifting the criteria weights and producing different weighting scenarios. The fuzzy VIKOR method was analyzed for different values of the υ variable. Results: Weighting of criteria by AHP showed that the distance from surface water with a weight coefficient of 0.200 was the most important one. according to the required minimum area, six alternatives were extracted from the weighted linear combination method. Conclusion: The sensitivity analysis of both methods showed that 405 hectares in the Southeast of Kashan are the best ones to establish industries.

PUGH Decision Trapezoidal Fuzzy and Gradient Reinforce Deep Learning for Large Scale Requirement Prioritization

Objective: To prioritize requirements for large scale software projects within time involving uncertainty in the opinions among different stakeholders. Methods: We propose Pugh Trapezoidal Fuzzy and Gradient Reinforce Learning (PTF-GRL) methods for large scale software requirement prioritization. A Pugh Decision-based Trapezoidal Fuzzy Requirement Selection model is designed, inputting the functional and non-functional requirements of the corresponding stakeholders. With the assistance of Trapezoidal Fuzzy Inference, the qualitative factors are mapped with the corresponding numeric factors, which increases the computational efficiency. Findings: Performance is analyzed based on four parameters: The first parameter is accuracy and our method showed improvement of 4%, 7% and 3% compared to JRD-SCRUM, IFS and SRPTackle respectively. The second parameter is prioritization time and found that our method had reduced time of 30%, 37% and 39% compared with existing methods. The third parameter is precision and it was found that our method improves precision by 6%, 10% and 5% compared with the other two methods. The final parameter we consider is the test suite execution and our method showed improvement of 12%, 19% and 5% compared with the existing two methods. Novelty/Applications: The originality of this work indicates the better performance along with the optimal test suite execution even considering the uncertainty factor in the proposed method compared with existing similar methods. Keywords: Software Project; Pugh Decision Matrix; Trapezoidal Fuzzy Inference; Gradient Orientation; Reinforce Learning; Requirement Prioritization

Influence of Covid-19 on Surgical Practice: An update is a need

Aim: To evaluate existing practice recommendations in the event of a COVID-19 pandemic and to develop future pandemic strategies. Methods: In the context of COVID-19, a review of surgical societies' and government agencies' websites, as well as current literature, was done to discover recommendations for plastic surgery operations. Our plastic surgery problems have been divided into four areas, with guidelines developed for each. Results: This pandemic demands modification in clinical practice and transformations are recommended in outpatients and peri-operative settings to minimize the potential risk of disease transmission. Prioritization methods, operating room atmosphere, health care personnel safety, and preoperative patient evaluation are all covered by consensus standards for Plastic and Reconstructive Surgery. Operation theatre should be modified to minimize the disease transmission. PPE use is marked critical for all health care personals working in emergency settings. Preoperative assessment should be a critical component of management strategy. Conclusion: Since COVID-19 is predicted to resurface, doctors must weigh the risks and benefits of each operation in relation to essential patient care, COVID-19 spread reduction, and health-care worker safety. The plastic surgeon will be guided by scientific evidence-based standards that include conscious decision-making in emergency and non-urgent patients, OR changes, PPE usage, and preoperative screening tools. Keywords: Safety guidelines, plastic surgical procedures, COVID-19

Simultaneous carbon storage in arable land and anthropogenic products (CSAAP): Demonstrating an integrated concept towards well below 2°C

Additional carbon dioxide removals from the atmosphere are indispensable for controlling global warming. This study introduces two concepts: (i) ‘biopump’, plants capable of significantly transferring carbon into soils, and (ii) Carbon Storage in Arable land and Anthropogenic Products (CSAAP), the cultivation of biopumps on low-impact target areas and their conversion into long-lived anthropogenic products. It develops and demonstrates a 4-step framework applicable to any region, that allows illustrating the long-term climate effect of CSAAP strategies applied to a selected territory, tracking carbon flows, from biopump cultivation to biomaterials manufacturing and their end-of-life. Based on a list of twenty-seven biopumps uncovered from a literature review, this study first proposes a method for the regional prioritization of biopumps, considering among others their ability to increase soil organic carbon (SOC) and adaptation potential. It then demonstrates how to select target areas on a territory, and shows how to integrate the output of SOC simulations in dynamic carbon balances, that can be translated in terms of effects on global mean temperature change. This is illustrated for products encompassing a variety of lifetimes, for the case of Miscanthus grown on French target areas. A potential ranging from 11,187- 24,007 km2 target areas was identified; translating in additional SOC stocks of 0.23 to 0.49 Mt year−1, which represents 0.19%- 0.41% of the annual French carbon budget, or 13% assuming an indefinite storage in anthropogenic products. We concluded that CSAAP strategies could induce negative emissions by 2100, with efficiency strongly depending upon carbon residence time in the technosphere.

Waste Classification of Spent Refractory Materials to Achieve Sustainable Development Goals Exploiting Multiple Criteria Decision Aiding Approach

The recycling of used refractory materials in the heavy industry constitutes one of the significant environmental problems in the industry related to environmental and financial issues. This study proposes a multicriteria methodological frame to characterize the refractory material waste and identify the recycling capabilities. Considering the chemical and physical analysis of the refractory material wastes, the proposed methodological frame progresses into a two-phase procedure. The first phase includes an on/off approach that allows discretizing the refractory material wastes to compatible or not compatible as far as their recycling prospects. Then, an additive value model is utilized, including (a) the marginal value functions used for every criterion related to critical environmental factors, and (b) the weight vector reflecting the relative importance of the criteria used. A group of experts concerning the environment and the refractory materials was employed to estimate the additive value model. The assessment of the marginal value function is achieved using the module of the Multicriteria Interactive Intelligence Decision Aiding System (MIIDAS), which is based on a modification of the mid-value split point technique incorporating focused dialogues, artificial intelligence, and visual techniques. The weight vector was assessed using the weight assessment through prioritization method (WAP), which concludes with the estimation of the weights based on the criteria ranking and the pairwise expression of the strength of preferences for the consecutive criteria according to their ranking. The outcome of this approach is to introduce an environmental appropriateness index for refractory materials based on their chemical composition and the judgement of an expert group. The main findings of this research may be useful for engineers, decision-makers, and scientists in the field of circular economy and waste management.

Fault Handling in Industry 4.0: Definition, Process and Applications.

The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.

A prioritization approach of non-reciprocal fuzzy preference relations and its extension

In decision making problems, preference relations are the widely used tools to simulate the opinions of decision makers (DMs). It is important to elicit priorities from a preference relation in order to reach an optimal solution. In this paper, we propose an optimization-based method to derive the priority vector from a fuzzy preference relation (FPR) without additively reciprocal property (ARP). First, the concept of non-reciprocal fuzzy preference relations (NrFPRs) is used to characterize the case of FPRs without ARP. An interval additive reciprocal matrix (IARM) is decomposed into two NrFPRs. Second, the variance similarity measure is proposed to quantify the similarity degree of two column/row vectors of NrFPRs. A double variance consistency index is constructed to measure the inconsistency degree of NrFPRs. An optimization-based model is proposed to elicit the priority vector from NrFPRs. Some interesting properties are addressed to show the effectiveness of the proposed approach. Third, the proposed prioritization method is extended to derive priorities from IARMs. Some comparisons with the existing prioritization methods of IARMs are reported using numerical examples. The observations reveal that the uncertainty exhibiting in IARMs can be captured by NrFPRs. Some decision-making models with IARMs could be revisited using the proposed method for NrFPRs.

Test case prioritization with neuron valuation based pattern

• Filtering error-triggering data in testing DNNs is cost-effective. • The set of test cases may contain various types of data. • Patterns for DNN-based classifiers can help in prioritizing the abnormal inputs. • Our pattern consists of neurons and the computed thresholds for pattern matching. • Evaluation results show our advantage in prioritizing error-triggering samples. Deep neural networks (DNNs) have been increasingly adopted in various applications. Systematic verification and validation are essential techniques to guarantee the quality of such systems. Testing is one of the feasible solutions for system validation. However, testing DNNs usually requires a large number of test cases and leads to the high labeling cost. Meanwhile, the test cases may contain various noisy data, which also increases the burden of testing. In this paper, we put forwards a test case prioritization method for DNN-based classifiers, which assigns high priorities to those cases that can lead to problematic classifications. The test cases are prioritized w.r.t. the pattern extracted from the neuron outputs. The difference between the pattern acquired from the training set and the neurons matched from a given input determines the priority of the input. For a trained model, the method consists of two steps. First, we collect neuron output values over the training set and construct a neuron-based pattern for every class of training samples. Second, the metrics are computed according to the comparison between neuron output values of the DNN model from an input and the selected pattern. We carry out the experimentation over three popular datasets with various neural network structures. The extensive experimental results demonstrate that the efficiency and the generalizability of the prioritization method outperform most of the existing techniques.

Plant invasion risk in the Marine Protected Area of Tavolara (Italy)

Invasions by alien plants pose a significant threat to biodiversity, having negative impacts on species richness, community, and ecosystems. Protected areas and small islands could be particularly affected by the presence of non-native plants that pose high threats to their biodiversity. Several surveys were made under the life project “Puffinus Tavolara” (LIFE12 NAT/IT/000416) between 2014 and 2015 to detect the presence of non-native plant species in the Marine Protected Area of Tavolara (Sardinia, Italy). The aim of the present work was to produce an updated inventory of the alien flora of the Island of Tavolara and the nearby Isola Piana. A list of 58 non-native taxa was produced, assessing their accidental or voluntary introduction, residence and invasive status, and applying an adapted A-WRA and EPPO prioritisation methods on the alien flora of these islands. Importantly, the results of the two methods applied are quite different, but they are both useful and informative for the prioritisation and management of invasive alien plants.

Novel methods for spatial prioritization with applications in conservation, land use planning and ecological impact avoidance

Abstract Spatial (conservation) prioritization integrates data on the distributions of biodiversity, costs and threats. It produces spatial priority maps that can support ecologically well‐informed land use planning in general, including applications in environmental impact avoidance outside protected areas. Here we describe novel methods that significantly increase the utility of spatial priority ranking in large analyses and with interactive planning. Methodologically, we describe a novel algorithm for implementing spatial priority ranking, novel alternatives for balancing between biodiversity features, fast tiled FFT transforms for connectivity calculations based on dispersal kernels, and a novel analysis output, the flexibility map. Marking by N the number of landscape elements with data, the new prioritization algorithm has time scaling of less than Nlog2N instead of the N2 of its predecessor. We illustrate feasible computation times with data up to billions of elements in size, implying capacity for global analysis at a resolution higher than 0.25 km2, or close to 1‐ha resolution for a continent. The algorithmic improvements described here bring about improved capacity to implement decision support for real‐world spatial conservation planning problems. The methods described here will be at the technical core of forthcoming software releases.

Probabilistic weighted voting model using multiple machine learning methods for fault detection and classification

PurposeThe individual machine learning methods used for fault detection and classification have accuracy performance at a certain level. A combined learning model composed of different base classifiers rather than an individual machine learning model is introduced to ensure diversity. In this way, this study aims to improve the generalization capability of fault detection and classification scheme.Design/methodology/approachThis study presents a probabilistic weighted voting model (PWVM) with multiple learning models for fault detection and classification. The working principle of this study’s proposed model relies on weight selection and per-class possibilities corresponding to predictions of base classifiers. Moreover, it can improve the power of the prediction model and cope with imbalanced class distribution through validation metrics and F-score.FindingsThe performance of the proposed PWVM was better than the performance of the individual machine learning methods. Besides, the proposed voting model’s performance was compared with different voting mechanisms involving weighted and unweighted voting models. It can be seen from the results that the presented model is superior to voting mechanisms. The performance results revealed PWVM has a powerful predictive model even in noisy conditions. This study determines the optimal model from among voting models with the prioritization method on data sets partitioned different ratios. The obtained results with statistical analysis verified the validity of the proposed model. Besides, the comparative results from different benchmark data sets verified the effectiveness and robustness of this study’s proposed model.Originality/valueThe contribution of this study is that PWVM is an ensemble model with outstanding generalization capability. To the best of the authors’ knowledge, no study has been performed using a PWVM composed of multiple classifiers to detect no-faulted/faulted cases and classify faulted phases.

Transparency of high-dimensional propensity score analyses: Guidance for diagnostics and reporting.

PurposeThe high‐dimensional propensity score (HDPS) is a semi‐automated procedure for confounder identification, prioritisation and adjustment in large healthcare databases that requires investigators to specify data dimensions, prioritisation strategy and tuning parameters. In practice, reporting of these decisions is inconsistent and this can undermine the transparency, and reproducibility of results obtained. We illustrate reporting tools, graphical displays and sensitivity analyses to increase transparency and facilitate evaluation of the robustness of analyses involving HDPS.MethodsUsing a study from the UK Clinical Practice Research Datalink that implemented HDPS we demonstrate the application of the proposed recommendations.ResultsWe identify seven considerations surrounding the implementation of HDPS, such as the identification of data dimensions, method for code prioritisation and number of variables selected. Graphical diagnostic tools include assessing the balance of key confounders before and after adjusting for empirically selected HDPS covariates and the identification of potentially influential covariates. Sensitivity analyses include varying the number of covariates selected and assessing the impact of covariates behaving empirically as instrumental variables. In our example, results were robust to both the number of covariates selected and the inclusion of potentially influential covariates. Furthermore, our HDPS models achieved good balance in key confounders.ConclusionsThe data‐adaptive approach of HDPS and the resulting benefits have led to its popularity as a method for confounder adjustment in pharmacoepidemiological studies. Reporting of HDPS analyses in practice may be improved by the considerations and tools proposed here to increase the transparency and reproducibility of study results.

Sourcing product innovation intelligence from online reviews

In recent years, online reviews have offered a rich new medium for consumers to express their opinions and feedback. Product designers frequently aim to consider consumer preferences in their work, but many firms are unsure of how best to harness this online feedback given that textual data is both unstructured and voluminous. In this study, we use text mining tools to propose a method for rapid prioritization of online reviews, differentiating the reviews pertaining to innovation opportunities that are most useful for firms. We draw from the innovation and entrepreneurship literature and provide an empirical basis for the widely accepted attribute mapping framework, which delineates between desirable product attributes that firms may want to capitalize upon and undesirable attributes that they may need to remedy. Based on a large sample of reviews in the countertop appliances industry, we demonstrate the performance of our technique, which offers statistically significant improvements relative to existing methods. We validate the usefulness of our technique by asking senior managers at a large manufacturing firm to rate a selection of online reviews, and we show that the selected attribute types are more useful than alternative reviews. Our results offer insight in how firms may use online reviews to harness vital consumer feedback.

Multi-criteria and Multi-expert Requirement Prioritization using Fuzzy Linguistic Labels

Requirement prioritization in Software Engineering is the activity that helps to select and order for the requirements to be implemented in each software development process iteration. Thus, requirement prioritization assists the decision-making process during iteration management. This work presents a method for requirement prioritization that considers many experts' opinions on multiple decision criteria provided using fuzzy linguistic labels, a tool that allows capturing the imprecision of each experts' judgment. These opinions are then aggregated using the fuzzy aggregation operator MLIOWA considering different weights for each expert. Then, an order for the requirements is given considering the aggregated opinions and different weights for each evaluated dimension or criteria. The method proposed in this work has been implemented and demonstrated using a synthetic dataset. A statistical evaluation of the results obtained using different t-norms was also carried out.

Fish zeta diversity responses to human pressures and cumulative effects across a freshwater basin

AbstractAimDeclining biodiversity across ecosystems and myriad human pressures necessitate high‐level regional assessments for effective management. Evaluation of biodiversity patterns and stressor accumulation through beta diversity and cumulative effect analyses are two key methods for management prioritization. This study links these concepts to develop a novel cumulative effect metric based on beta diversity responses.LocationFraser River basin, British Columbia, Canada.MethodsMulti‐Site Generalized Dissimilarity Models were used to evaluate nonlinear relationships between fish species compositional differences (ζn, number of shared species across any number of watersheds compared) and human pressure, environmental, and geospatial differences among all watersheds and within low, mid‐, and high elevation clusters. A cumulative effect metric was calculated as the sum of response values generated by the model for each human pressure variable specific to each watershed, when evaluated for ζ2 (equivalent to pairwise beta diversity). This metric was tested against the local contribution of each watershed to beta diversity to determine whether watersheds with unique communities had low cumulative effects and are, therefore, candidates for conservation and conversely, whether watersheds with non‐distinctive communities had high cumulative effects and warrant restoration. Species contributions to beta diversity were also assessed across the basin.ResultsZeta diversity across low elevation watersheds indicated stronger filtering by human pressures than mid‐ and high elevation watersheds, which showed more stochastic community assembly. The relative importance and response to human pressures varied based on the diversity component (i.e., total diversity including compositional nestedness vs. turnover) and order of zeta (number of watersheds compared). Cumulative effects were negatively related to community uniqueness, supporting the use of these metrics for developing management priorities.Main ConclusionsThis assessment contributes to biodiversity conservation efforts by identifying important watersheds, species, and human pressures to manage as well as providing a cumulative effect metric directly based on biodiversity responses.

Integrated multi-hazard risk to social-ecological systems with green infrastructure prioritization: A case study of the Yangtze River Delta, China

The sustainable future of deltas is increasingly threatened due to natural ecosystem degradation and increasing climate change hazards. Thus, the spatial prioritization of Green Infrastructure (GI) is of great importance for ecosystem conservation and climate adaptation. The existing GI prioritization methods in deltaic regions lack a systematic method of incorporating the state of vulnerability and risk. In this study, a novel method was developed for systematically prioritizing GI management zones based on the biodiversity and ecosystem services that addresses the clear relevance to multi-hazard risk to the social-ecological system (SES). The approach was demonstrated in the Yangtze River Delta, China. The multi-hazard risk to the SES in the county-level administrative regions was evaluated based on the Global Delta Risk Index (GDRI). The outputs were then transferred into a critical cost factor in a combined spatial prioritization of the biodiversity and ecosystem services using the Marxan with Zones software to determine the spatial priorities of multiple management zones (the core zone, conservation zone, and sustainable use zone). Two comparative scenarios were run to illustrate how the inclusion of the multi-hazard risk can affect the spatial prioritization of GI. Based on our results, integrating the multi-hazard risk of the SES can effectively coordinate the configuration of GI management zones. This supports our conclusion that the risk to the SES should always be considered during future GI prioritization to ensure the GI network s resilience to multiple climatic changes. Our proposed framework will help guide future applications of GI in global deltas and the implementation of flexible conservation management in a multi-hazard context.