Criteria Importance Through Intercriteria Correlation Research Articles

Supplier selection of companies providing micro mobility service under type-2 neutrosophic number based decision making model

Environmental approaches play an active role in the production platform as well as in the supply chain. Studies that deal with supplier selection problems in the supply chain with a green focus have recently intensified. In this research, the supplier selection problem of companies providing “last mile” service, which is seen as the last step of transportation, is handled with a green focus. The supplier selection problem of these companies is differentiated according to strategic and non-strategic product groups. In this study, a green-oriented supplier selection proposal is presented for both the strategic product group and the non-strategic product group. The supplier selection problem is accepted as a multi criteria decision making problem (MCDM). Thus, the type-2 neutrosophic numbers (T2NNs) - based CRiteria Importance Through Intercriteria Correlation (CRITIC) – based Multi-Attributive Border Approximation Area Comparison (MABAC) hybrid model is applied to evaluate the green-oriented supplier. The motivation of the study is to determine the best alternative supplier type for strategic and non-strategic products groups. In this context, two cases are created, ten criteria are selected, and three supplier types are determined. Supplier types are as follows: (i) Classical suppliers, (ii) Hybrid-Classical to green centric suppliers, and (iii) Start-Ups & green centric suppliers. To devise a solution for addressing the research problem, the selection of the optimal green supplier type was accomplished through a recommended T2NN-CRITIC-MABAC hybrid model. Accordingly, for both strategic and non-strategic product groups, micro-mobility service providers identified Hybrid-Classical to Green Centric Suppliers as the most suitable green supplier type. In addition, the findings are strengthened by creating twenty-two sensitivity analysis scenarios. In conclusion, the T2NN-CRITIC-MABAC hybrid method introduced in this study represents a methodologically diverse, fuzzy logic-based approach with practical implications for supplier selection in the micro mobility services sector. Its application and empirical testing contribute to the literature and provide valuable insights for decision-makers in the industry.

Embracing fairness within a cross-efficiency hierarchical network DEA system

Several scholars have utilized hierarchical network Data Envelopment Analysis modeling techniques to assess the performance of complex structures. However, there has been limited consideration given to the integration of a peer-appraisal setting within a self-evaluation hierarchical context. This aims to enhance discriminatory power and mitigate the issue of unrealistic weighting scheme. To this end, our study extends the single-stage hierarchical additive self-evaluation model of Kao (Omega 51:121–127, 2015. https://doi.org/10.1016/j.omega.2014.09.008), by integrating the well-established cross-efficiency method. An original combination of a maxmin secondary goal model and the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is proposed, to expand the basic hierarchical self-evaluation model. The maxmin model addresses the issue of the non-unique optimal multipliers obtained from the self-evaluation model, ensuring a more realistic weight scheme. The CRITIC method, that tackles the aggregation problem by objectively determining weights of criteria, rewards the minority and is conducive to a fairer evaluation. Results indicate that the proposed approach is more likely to obtain a unique efficiency and ranking score for the units under consideration. This study entails a numerical experimentation aimed at evaluating the efficiency of a set of 20 universities while validating the applicability of our proposed approach. To conclude, the practical applications of this methodological framework could encompass assessing services within the higher education sector or fostering sustainable development across various operations within a hierarchical structure.

Evaluation of sustainable development and utilization-oriented renewable energy systems based on CRITIC-SWARA-CODAS method using interval valued picture fuzzy sets

Multi-criteria decision making (MCDM) methods used to overcome complex real-life problems is the process of finding the most suitable alternative among alternative options under multiple tangible or intangible criteria. In particular, the use of MCDM methods is an important tool for policy makers, managers and decision makers (DMs) to easily and effectively address today's MCDM problems such as renewable energy. However, since the classical MCDM methods are based on crisp numerical values, they are insufficient when considering insufficient information about the systems, complex environment and real-life conditions. Especially since many real-life problems within the scope of sustainability contain uncertain information, using them together with fuzzy sets is extremely important for making the right decisions. This study presents a new model based on the interval-valued picture fuzzy (IVPF)-MCDM integrated approach in an environment of uncertainty for the evaluation of sustainable development and utilization oriented renewable energy systems. In the study, firstly, renewable energy alternatives focused on sustainable development and utilization for Turkey and the criteria affecting them have been determined. Secondly, for the objective and subjective weights of the criteria, the CRiteria Importance Through Intercriteria Correlation (CIRITIC) and Stepwise Weight Assessment Ratio Analysis (SWARA) methods have been extended to interval-valued picture fuzzy sets (IVPFSs), respectively, and the weights have been obtained. Then, the weights have been combined by the coefficient parameter determined by the expert. Later, the COmbinative Distance-based ASsessment (CODAS) method has been extended to IVPFSs to rank renewable energy alternatives focused on sustainable development and use under uncertainty. A comprehensive sensitivity analysis has been conducted to confirm the stability and effectiveness of the method. Moreover, a comparative analysis has been presented with other MCDM methods in the literature showing the superiority and advantages of the developed method.

Analytical optimization of open hole effects on the tensile properties of SS400 sheet specimens using an integrated FFD-CRITIC-DFA method

Structural components are generally composed of material discontinuities, including open holes, which are considered stress concentrators in engineering components. In view of this, assessing the influence of open holes on the tensile properties is crucial to determine the sensitivity and tensile strength of a particular material. Nevertheless, investigation of the impact of open holes on the tensile properties of SS400 steel sheets is very limited and yet to be explored. Therefore, this study was performed to optimize the effects of open holes on the tensile properties of SS400 sheet specimens based on a Full Factorial Design (FFD) experiment. Four input parameters that represent various hole configurations, which include the hole diameter, location of the hole, number of holes, and hole shape, were considered in this study to develop the experimental-based prediction models to optimize the output performance, namely yield strength, ultimate tensile strength, and ultimate elongation, commonly denoted as YS, UTS, and UE respectively. A total of 10 additional experimental trials were then utilized to verify the constructed models. In addition, the weight fractions for YS, UTS, and UE were identified using the Criteria Importance Through Inter-Criteria Correlation (CRITIC) method. Subsequently, the Desirability Function Analysis (DFA) is utilized to pinpoint the optimal parameter conditions for maximizing the tensile properties. Based on the results, all four parameters showed significant effects on the response variables, except the number of holes for UTS and hole location for UE. The diameter also recorded the highest contribution toward UTS and UE, followed by the hole shape. Regarding YS, hole diameter takes precedence, with the number of holes as the second most influential factor. Furthermore, the average absolute percent deviation for the prediction responses of 10 experimental cases were 1.06 %, 0.90 %, and 0.85 % for YS, UTS, and UE, respectively, confirming the validity of the constructed models. Meanwhile, the CRITIC method estimated the weight fractions for YS, UTS and UE from the experimental data, which were 0.3825, 0.2559, and 0.3616, respectively. The DFA-derived composite desirability, rated at 0.9820, suggests optimal conditions: a 1 mm hole diameter, centered hole location, three holes, and a hexagonal shape. The minimal deviations between predicted and experimental values affirm the robustness of the models. Overall, this investigation yields important insights for optimizing open holes and elevating the tensile performance of SS400 sheet specimens.

An Integrated Neutrosophic Approach for the Urban Energy Internet Assessment under Sustainability Dimensions


 The urgent issue of climate change has resulted in an increasing preoccupation with the transition to low-carbon energy. The urban energy internet (UEI) enables the efficient utilization of renewable energy via the integration of contemporary energy grids, smart energy services, and cyber-physical systems. Consequently, the assessment of urban energy is vital for the construction of low-carbon cities. This study intends to provide an integrated decision-making approach for addressing assessment challenges related to UEI, with a focus on sustainability. The introduced approach adopts the opinions of three experts to express their opinions through semantic terms in evaluating sustainability factors and evaluating UEI projects. Determining the most suitable project for sustainable UEI is a difficult task. In addition, there are multiple sustainability dimensions, including: economic, social, technical, environmental and resource dimension. This paper presents an integrated approach through which experts can use semantic terms to express their opinions to evaluate the priorities of sustainability dimensions that affect the sustainability of UEI projects. Therefore, this study applies a hybrid multi-criteria decision making (MCDM) approach that takes into account multiple dimensions. Also, the uncertainty in the study is handled by type 2 neutrosophic numbers (T2NNs). Initially, the CRiteria Importance through Inter-criteria Correlation (CRITIC) method was used to determine the relative importance of the sustainability dimensions used in the study. Also, the evaluation based on distance from average solution (EDAS) method was used to rank the selected alternatives. The results indicate that the technical dimension is the most important with a weight of 0.384. Finally, a sensitivity analysis was conducted to prove the validity and reliability of the developed framework with differences in weights.

Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages

Efficient routing in urban vehicular networks is essential for timely and reliable safety message transmission, and the selection of paths and relays greatly affects the quality of routing. However, existing routing methods usually face difficulty in finding the globally optimal transmission path due to their greedy search strategies or the lack of effective ways to accurately evaluate relay performance in intricate traffic scenarios. Therefore, we present a vehicular safety message routing method based on heuristic path search and multi-attribute decision-making (HMDR). Initially, HMDR utilizes a heuristic path search, focusing on road section connectivity, to pinpoint the most favorable routing path. Subsequently, it employs a multi-attribute decision-making (MADM) technique to evaluate candidate relay performance. The subjective and objective weights of the candidate relays are determined using ordinal relationship analysis and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting methods, respectively. Finally, the comprehensive utility values of the candidate relays are calculated in combination with the link time and the optimal relay is selected. In summary, the proposed HMDR method is capable of selecting the globally optimal transmission path, and it comprehensively considers multiple metrics and their relationships when evaluating relays, which is conducive to finding the optimal relay. The experimental results show that even if the path length is long, the proposed HMDR method gives preference to the path with better connectivity, resulting in a shorter total transmission delay for safety messages; in addition, HMDR demonstrates faster propagation speed than the other evaluated methods while ensuring better one-hop distance and one-hop delay. Therefore, it helps to improve the performance of vehicular safety message transmission in intricate traffic scenarios, thus providing timely data support for secure driving.

Parametric optimization of an impingement jet solar air heater for active green heating in buildings using hybrid CRITIC-COPRAS approach

This work aimed to optimize the parameters of discrete multi-arc shaped ribs (DMASRs) in a solar air heating system (SAHS) through multi-criteria decision-making techniques. In the experiment, the roughness parameters of DMASRs were varied to find the best parameter combination for optimal SAHS performance. The relative rib height (Hr/H) was varied from 0.025 to 0.047 , and the relative rib pitch (Pr/H) was varied from 0.58 to 3.1. The results obtained for the Nusselt number and friction factor, which determine the performance of the SAHS system, depend on the geometrical parameters of the roughness. The parameters of DMASRs did not show any discernible trend. Hence, a multi-decision criteria approach that uses criteria importance through inter-criteria correlation (CRITIC) and complex proportional assessment (COPRAS) hybrid techniques was employed to determine the best parameter combination for optimal performance. The novel aspect of this study includes the use of a hybrid method (experimental and analytical) to optimize the performance of SAHS roughened with DMASRs hindrance promoters and predictions of outcomes using a hybrid CRITIC-COPRAS approach. The experimental and analytical examination through the use of the hybrid CRITIC-COPRAS approach is an essential component of this research that contributes to the optimization of the design parameters of such SAHS. The finding demonstrated that when Re = 19000, Pr/H = 1.7, and Hr/H = 0.047 were reached, the SAHS obtained an optimal thermohydraulic performance of 4.1.

Landscape Sensitivity Assessment of Historic Districts Using a GIS-Based Method: A Case Study of Beishan Street in Hangzhou, China

Historic districts may be damaged during urban renewal. Landscape sensitivity can be used as a method to judge the ability of a landscape to resist change. This study proposes an improved method for assessing landscape sensitivity based on a geographic information system (GIS) according to the characteristics of historic districts. Based on a previous method, this study adds POI big data for comprehensive evaluation and uses objective criteria importance through intercriteria correlation (CRITIC) statistics instead of subjective methods to determine the weights. The assessment framework uses ecological, visual, and cultural sensitivity as primary criteria, which are further defined by several sub-criteria. The Beishan Street Historic District in Hangzhou, China, is used as a case study, and the results of the assessment are shown in the form of sensitivity maps. The results show that the maps can identify buildings in areas of high sensitivity and provide objective indicators for future conservation. Based on the sensitivity maps, this study innovatively used correlation analysis to reveal important interrelationships between ecological, visual, and cultural sensitivity. Assessment factors such as land use type need to be prioritized because they are more closely linked to other factors.

Enhancing risk factor evaluation for infectious diseases using decision-making approach based on linear programming

Infectious diseases are defined as the invasion of hazardous agents into the human body that causes injury and has the potential to spread to others. These diseases cause a considerable proportion of illnesses and deaths in any given community. The criteria importance through inter-criteria correlation (CRITIC) approach is used to determine the relative importance of multiple assessment criteria, whereas combinative distance-based assessment (CODAS) is a methodology used to compare and evaluate the efficacy of numerous options. The CRITIC-CODAS method is a linear programming-based tool for estimating the risks of infectious diseases. This method takes into account a number of aspects, including the disease’s characteristics, the population at risk, and the available resources for prevention and treatment. The CRITIC-CODAS technique can provide valuable insights into the possible impact of an infectious disease outbreak and aid in improving decision-making regarding prevention and control measures by assessing these elements in a systematic and quantitative manner. The purpose of this work is to provide an overview of the CRITIC-CODAS technique, which is powered by a q-rung orthopair fuzzy set (q-ROFS), and its use in estimating the risk of infectious disease outbreaks. According to the case study, the infectious disease malaria has a greater possibility of spreading in the city under observation. According to the proposed method, the ranking of infectious cities given as ℐ3ℷ≻ℐ4ℷ≻ℐ5ℷ≻ℐ2ℷ≻ℐ1ℷ. This implies that the likelihood of an epidemic of the infectious disease ℐ3ℷ is higher in the city being observed. The study also addresses the approach’s benefits and shortcomings and makes recommendations for further research and development. Overall, the CRITIC-CODAS approach appears to be a useful tool for better understanding infectious disease risks and guiding appropriate response tactics.

Extension Cloud Model and Grey Clustering Evaluation of Enterprise Safety Management System: Based on COWA-CRITIC Combination Weighting

In order to address the issues of unclear risk grading control, lack of safety management, and hidden danger investigation and management processes, this paper used a mining enterprise as the backdrop for an engineering example. The “evaluation model of the overall construction level of the enterprise safety management system” is constructed from four aspects: “preliminary infrastructure”, “risk grading and control”, “hidden danger investigation and management processes”, and “Post-support work”. The safety evaluation level is divided into five levels, and the evaluation model is combined weighted by using the combined ordered weighted averaging (COWA) algorithm and the criteria importance through intercriteria correlation (CRITIC) method. In addition, the cloud model, the extension cloud model, and the grey clustering evaluation method are used for a thorough evaluation. Finally, the enterprise safety management system’s overall construction level is determined to be good. In order to effectively strengthen the enterprise safety management capability and prevent the occurrence of production safety accidents, this study provides a practical and thorough evaluation method for the evaluation of the enterprise safety management system. This method makes it easier to identify system weaknesses and provides a safety guarantee for the sustainable development of enterprises.

Selecting the ideal sustainable green strategy for logistics companies using a T-spherical fuzzy-based methodology

Governments, institutions, and organizations focused on renewable and green energy problems have provided significant support and incentives for project development in recent years. Green transformation is fostered through environmentally friendly and clean energy practices, which are the key to a sustainable future for the entire world, and governments and enterprises shape their business and transactions within this framework. However, there are various challenges and obstacles in green energy applications, both in businesses and in government policies. The study, which focuses on green energy problems, seeks to connect energy with sustainable business strategies. Furthermore, because businesses are often concerned with social and environmental implications, it is believed that implementing a sustainable energy strategy will give long-term benefits to businesses. In this regard, this study aims to identify green energy problems in logistics companies and to select the best sustainable strategy. The hybrid T-spherical fuzzy (T-SF) methodology is introduced to solve the problem, including T–SF–subjective weighting, T–SF–criteria importance through intercriteria correlation (CRITIC), subjective and objective weight integrated approach (SOWIA), and T–SF–additive ratio assessment system (ARAS). According to the results, the most important green energy factor in logistics companies is determined as “energy security”. The best sustainable strategy is identified as the “socially beneficial services supply strategy”. The relevant results are critical for guiding companies, users, and stakeholders.

A Data-Driven Comprehensive Battery SOH Evaluation and Prediction Method Based on Improved CRITIC-GRA and Att-BiGRU

The state-of-health (SOH) of lithium-ion batteries has a significant impact on the safety and reliability of electric vehicles. However, existing research on battery SOH estimation mainly relies on laboratory battery data and does not take into account the multi-faceted nature of battery aging, which limits the comprehensive and effective evaluation and prediction of battery health in real-world applications. To address these limitations, this study utilizes real electric vehicle operational data to propose a comprehensive battery health evaluation indicator and a deep learning predictive model. In this study, the battery capacity, ohmic resistance, and maximum output power were initially extracted as individual health indicators from actual vehicle operation data. Subsequently, a methodology that combines the improved criteria importance through inter-criteria correlation (CRITIC) weighting method with the grey relational analysis (GRA) method is employed to construct the comprehensive battery health evaluation indicator. Finally, a prediction model based on the attention mechanism and the bidirectional gated recurrent unit (Att-BiGRU) is proposed to forecast the comprehensive evaluation indicator. Experimental results using real-world vehicle data demonstrate that the proposed comprehensive health indicator can provide a thorough representation of the battery health state. Furthermore, the Att-BiGRU prediction model outperforms traditional machine learning models in terms of prediction accuracy.

A multi-objective bi-level task planning strategy for UUV target visitation in ocean environment

Unmanned underwater vehicle (UUV) is commonly utilized for ocean resource exploration. To effectively plan long-term tasks, it is crucial to consider energy usage and task quality. This paper proposes a multi-objective bi-level task planning strategy (MOBTPS) for solving an UUV dispatched to visit a set of targets. On the one hand, rapid initialization screening method based on task quality is adopted. On the other hand, to address the challenge of black-box optimization for UUV task time, a nested optimization approach is employed. The upper level of optimization focuses on determining the shortest access order for the tasks, while the lower level optimizes the route between the task points. The Simulated Annealing (SA) and Genetic Algorithm (GA) are selected for simultaneous optimization of task assignment and path planning. In order to adapt to varying ocean currents, an UUV control strategy is incorporated into the path planning process. The optimal solution is obtained by using the criteria importance through intercriteria correlation (CRITIC) method. The effectiveness of MOBTPS is demonstrated through extensive numerical simulations.

Attaining Sustainable Development Goals through embedding circular economy principles: Evidence from food processing small‐ and medium‐sized enterprises in India

AbstractVarious industries are gradually sifting away from the linear economy and toward the circular economy (CE), but its advancement is crawling far behind the environmental contamination by the food processing industry in developing countries. Hardly any research analyzes the CE performance of food processing small‐ and medium‐sized enterprises (FPSMEs) in an emerging economy context. Hence, developing a CE performance evaluation framework is imperative to facilitate this transition. This research proposes a comprehensive framework for evaluating CE performance based on three real‐world cases of Indian FPSMEs. Initially, 15 essential criteria are shortlisted from the literature and refined by the experts. Afterward, data are collected through questionnaires administered to experts and structured interviews. Next, this research employs a multi‐criteria decision‐making (MCDM)–based approach, in which the Criteria Importance Through Inter‐criteria Correlation (CRITIC) method is used to compute the objective weights of criteria and the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is used to determine the performances of FPSMEs and rank them accordingly. The results reveal that “investment in Corporate Social Responsibility (CSR),” “use of renewable energy,” “increase in scrap recycling rate,” “total emission,” and “total water consumption” are the top five criteria for CE performance. Investment in CSR emerges as the most influential criterion for strategic corporate transformation, which blends the notions of CE and CSR as a feasible solution for designing circular business processes.

ECCANS: Enhanced CRITIC-based Context-Aware Network Selection algorithm for 5G HetNet

The ability of mobility management to provide seamless communication and better quality of service (QoS) to the multiservice multimode mobile node (MMMN), which can run multiple services simultaneously during roaming in ultra-dense heterogeneous networks with different radio access technologies, is a major challenge in implementing 5G and beyond. Ultra-dense heterogeneous networks (HetNet), restrictions on multiservice multimode mobile terminals, and user mobility all lead to a number of serious problems, such as the ping-pong effect, unnecessary handovers, and decreasing cell edge spectral efficiency. The dense deployment of small base stations also causes a high probability of frequent handovers in HetNet, which has led to a significant escalation in energy consumption resulting from unnecessary signaling, increased network overhead, and higher energy usage by network equipment. This increased energy requirement may lead to potentially increase the emissions of greenhouse gases to meet the energy production needs. Several handover decision algorithms have been proposed, but most of them deal with only running a single service at a time on a mobile node. In this paper, authors propose a new network selection model for MMMN which may run multiple services simultaneously. This model uses context-aware information about the user’s preferences, characteristics, and needs of the services, limitations of mobile terminal, and network conditions of an ultra-dense HetNet to choose the best network. The fundamental principle of this model is based on the comprehensive network criteria weight computed by the integration of Enhanced CRiteria Importance Through Inter-criteria Correlation (CRITIC), fuzzy analytic hierarchy process (FAHP), and group decision-making (GDM) techniques and the service priority of running services. For computing the objective weight of criteria, a novel improved CRITIC method has been proposed. At the same time, sigmoid utility functions and linear utility functions have been used to figure out the value of network criteria for running multiple services on the mobile node. Finally, the best network from HetNet for MMMN is selected using the technique for order performance by similarity to ideal solution (TOPSIS) and threshold by computing a comprehensive utility value network criteria decision matrix and comprehensive network criteria weight. The simulation results show that the proposed model increases the selection probabilities of an appropriate network according to the service requirements and network conditions. It also reduces unnecessary handovers and improves QoS while satisfying users’ preferences, service requirements, and terminal constraints, in solving the MMMN network selection problem.

Enhanced CRITIC-REGIME method for decision making based on Pythagorean fuzzy rough number

Rough set theory has revolutionized data analysis by exploring and extracting valuable insights from imprecise data. The concept of Pythagorean fuzzy sets is a relatively novel mathematical framework in the fuzzy family with a higher ability to deal with imprecision embedded in decision making. Both theories can be combined to develop a broader Pythagorean fuzzy rough numbers for maximum benefit. The CRiteria Importance Through Intercriteria Correlation (CRITIC) and REGIME methods have appeared as successful methods for estimating the weights of criteria and ranking alternatives, respectively, in the field of multi-criteria decision-making (MCDM). In this study, our main objective is to develop a new methodology based on Pythagorean fuzzy rough numbers by integrating CRITIC and REGIME methodologies for the first time. The proposed Pythagorean fuzzy rough CRITIC-REGIME methodology (PFR-CRITIC-REGIME) is implemented to solve the sustainable supply chain problem of an electric ferry in public transit. The proposed method is thoroughly compared with some other existing decision making methods. The results of these evaluations are shown very reliable results. A sensitivity analysis is also performed.

Material selection for sintered pulley in automobile: An integrated CRITIC-MARCOS model

Material selection plays a pivotal role in engineering and design, profoundly influencing product performance, cost, and sustainability. Traditional approaches to material selection typically involve an intricate interplay of multiple criteria, encompassing mechanical properties, environmental impact, cost, and availability. To grapple with this complexity, multi-criteria decision-making (MCDM) methods have risen to prominence as systematic frameworks for facilitating well-informed material selection decisions. MCDM methods offer a structured approach to evaluating and ranking materials based on a set of criteria, thereby empowering engineers and designers to make informed choices. In this paper, Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS) method has been employed to determine the most suitable material for sintered pulleys used in automobiles. CRiteria Importance Through Intercriteria Correlation (CRITIC) method is applied to assign criteria weights. The analysis reveals that sintered hardened steel emerges as the best choice for sintered pulleys in automotive applications. To validate the outcomes obtained from the proposed method, a performance analysis has been conducted, comparing the results with those generated by other well-established MCDM methods. Additionally, a sensitivity analysis has been carried out using Spearman rank correlation coefficient.

Two-stage site selection of hydrogen refueling stations coupled with gas stations considering cooperative effects based on the CRITIC-ITFAHP-MABAC method: A case study in Beijing

Powering hydrogen-fueled vehicles (HFVs) through hydrogen electrolysis using excess sustainable energy is an effective way to move towards sustainable development. Hydrogen refueling stations (HRSs) coupled with gas stations (GSs) can effectively integrate and utilize the resources in existing GSs, improve operational efficiency, and promote the cooperative income of the two. Aiming at the lack of cooperative effects between gas and hydrogen considered in the current site selection process for HRSs, a general criterion establishment process with refined measurement system are proposed to determine a more comprehensive criterion system, including the cooperative criteria and the precise calculation formula of each criterion. Based on these, a new two-stage site selection framework for HRSs is established. The first stage offers eligible alternatives through veto criteria. Then in the second stage, the Criteria Importance Through Intercriteria Correlation (CRITIC) and Improved Triangular Fuzzy Analytic Hierarchy Process (ITFAHP) methods are used to determine the objective weights and subjective weights of the criteria, and the results are combined according to the minimum deviation estimation (MDE). The rankings of alternatives are finally determined through the Multi-Attributive Border Approximation Area Comparison (MABAC) method. Three scenarios are proposed for further analysis in the case study of Beijing, China. The results show that the two-stage site selection framework with new criterion system makes the evaluation results more differentiated. When the importance coefficient of subjective weights reflecting the importance of the criterion in practice changes from 0 to 1, the score increase of the best alternative A5 (Zhoujiaxiangbei) is 49.14% higher than that of the second-ranked alternative, indicating that this alternative is more in line with people's perception of a good selection. The analysis results also further verify the rationality and robustness of the decision-making framework.

Dry forests conservation: A comprehensive approach linking ecosystem services to ecological drivers and sustainable management

Assessing ecosystem services supply is an efficient way to facilitate ecosystem management, but there is no clear way to link ecosystem services to sustainable management decision-making. This study links sustainable supply of ecosystem services to sustainable management goals to evaluate the conservation value of dry forests. Hence, 11 ecosystem services (water yield, timber production, fruit production, carbon sequestration, soil retention, pollination, aesthetic, tourism, heritage, soil organic matter and habitat provision) were quantified in three dry forest communities. Evergreen forest communities were more successful in supplying ecosystem services than other ones. Pearson correlation analysis showed that water yield and carbon sequestration had a trade-off relationship with pollination (P < 0.001). Total ecosystem services of plant communities were estimated by considering both trade-off and synergy relationships between ecosystem services. Precipitation (R2 = 0.465) and leaf lifespan (R2 = 0.422) were the main ecological drivers of ecosystem services based on Nonmetric multidimensional scaling (NMDS). Criteria importance through intercriteria correlation (CRITIC) showed that water yield and carbon sequestration services had the highest weights for sustainable management. Maximizing economic and social benefits was also the most important sustainable management criterion. The importance of plant communities for conservation was determined by integrating the relative importance of ecosystems for sustainable supply of services and the importance of services for sustainable management. The comprehensive approach presented in this study not only ecologically contributes to the ecosystem health and supports the sustainable supply of ESs, but also meets other goals of sustainable management and supports management decision-making towards optimization of sustainable supply of ESs.

Suitability Assessment of Multilayer Urban Underground Space Based on Entropy and CRITIC Combined Weighting Method: A Case Study in Xiong’an New Area, China

Suitability assessment is an essential initial step in the scientific utilization of underground space. It plays a significant role in providing valuable insights for optimizing planning and utilization strategies. Utilizing urban underground space has the potential to enhance the capacity of urban infrastructure and public service facilities, as well as mitigate issues such as traffic congestion and land scarcity. To effectively plan and utilize urban underground space, it is crucial to conduct a suitability assessment. This assessment helps identify the factors that influence the utilization of underground space and their impacts, offering guidance on avoiding unfavorable conditions and ensuring the safety of planned underground facilities. To achieve objective and reasonable evaluation results, this paper proposed an assessment method that combines entropy and CRITIC (CRiteria Importance Through Intercriteria Correlation) weighting. Taking Xiong’an New Area as a study area, a suitability assessment indicator system for underground space was established. The system included criteria indicators and sub-criteria indicators. By analyzing the weights, the study identified the difference of suitability and critical affecting factors for shallow, sub-shallow, sub-deep, and deep underground space. The results showed that deep layers had better suitability than shallow layers in the study area. The regions with inferior and worse suitability were mostly located around Baiyangdian Lake, with proportions of acreage at 54.69% for shallow layer, 42.06% for sub-shallow layer, 41.69% for sub-deep layer, and 42.03% for deep layer. Additionally, the dominant affecting factors of suitability varied in different layers of underground space. These findings provide valuable evidence for the scientific planning and disaster prevention of underground space in Xiong’an New Area, and also serve as references for studying suitability in other areas.