Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel-Alsina Function.

The COVID-19 pandemic led to an increase in healthcare waste (HCW). HCW management treatment needs to be re-taken into focus to deal with this challenge. In practice, there are several treatments of HCW with their advantages and disadvantages. This study is conducted to select the appropriate treatment for HCW in the Brcko District of Bosnia and Herzegovina. Six HCW management treatments are analyzed and observed through twelve criteria. Ten-level linguistic values were used to bring this evaluation closer to human thinking. A fuzzy rough approach is used to solve the problem of inaccuracy in determining these values. The OPA method from the Bonferroni operator is used to determine the weights of the criteria. The results of the application of this method showed that the criterion Environmental Impact ( C 4 ) received the highest weight, while the criterion Automation Level ( C 8 ) received the lowest value. The ranking of HCW management treatments was performed using MARCOS methods based on the Aczel-Alsina function. The results of this analysis showed that the best-ranked HCW management treatment is microwave (A6) while landfill treatment (A5) is ranked worst. This study has provided a new approach based on fuzzy rough numbers where the Bonferroni function is used to determine the lower and upper limits, while the application of the Aczel-Alsina function reduced the influence of decision-makers on the final decision because this function stabilizes the decision-making process.

Fermatean Fuzzy Schweizer-Sklar Operators and BWM-Entropy-Based Combined Compromise Solution Approach: An Application to Green Supplier Selection.

The Fermatean fuzzy set (FFS) is a momentous generalization of a intuitionistic fuzzy set and a Pythagorean fuzzy set that can more accurately portray the complex vague information of elements and has stronger expert flexibility during decision analysis. The Combined Compromise Solution (CoCoSo) approach is a powerful decision-making technique to choose the ideal objective by fusing three aggregation strategies. In this paper, an integrated, multi-criteria group-decision-making (MCGDM) approach based on CoCoSo and FFS is used to assess green suppliers. To begin, several innovative operations of Fermatean fuzzy numbers based on Schweizer-Sklar norms are presented, and four aggregation operators utilizing the proposed operations are also developed. Several worthwhile properties of the advanced operations and operators are explored in detail. Next, a new Fermatean fuzzy entropy measure is propounded to determine the combined weight of criteria, in which the subjective and objective weights are computed by an improved best-and-worst method (BWM) and entropy weight approach, respectively. Furthermore, MCGDM based on CoCoSo and BWM-Entropy is brought forward and employed to sort diverse green suppliers. Lastly, the usefulness and effectiveness of the presented methodology is validated by comparison, and the stability of the developed MCGDM approach is shown by sensitivity analysis. The results shows that the introduced method is more stable during ranking of green suppliers, and the comparative results expound that the proposed method has higher universality and credibility than prior Fermatean fuzzy approaches.

Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods.

Disposal of healthcare waste is a key issue of environmental sustainability in the world. The amount of healthcare waste is increasing every day, and it is necessary to adequately dispose of this kind of waste. There are various treatments for healthcare waste disposal, of which incineration of healthcare waste is one of the solutions. This paper suggests a model for selection of the type of incinerators that best solve the problem of healthcare waste in secondary healthcare institutions in Bosnia and Herzegovina. In the selection of incinerators, extended sustainability criteria were applied. Basic sustainability criteria: environmental, economic, and social criteria, were extended with the technical criterion. To assess which of the incinerators best meets the needs for healthcare waste collection, multi-criteria decision-making was used. For this purpose, a combination of two MCDA methods was applied in this paper, namely full consistency method (FUCOM) and compromise ranking of alternatives from distance to ideal solution (CRADIS). The FUCOM method was applied to determine the weights of the criteria, while the CRADIS method was applied to rank the alternatives. The best alternative of the six alternatives used is A2 (I8-M50), followed by alternative A1 (I8-M40), while the worst ranked alternative is A5 (I8-M100). These results were confirmed by applying the other six methods of multi-criteria analysis and the performed sensitivity analysis. The contribution of this paper is reflected through a new method of multi-criteria analysis that was used to solve decision-making problems. This method has shown simplicity and flexibility in operation and can be used in all problems when it is necessary to make a multi-criteria selection of alternatives.

MARCOS technique under intuitionistic fuzzy environment for determining the COVID-19 pandemic performance of insurance companies in terms of healthcare services.

Assessing and ranking private health insurance companies provides insurance agencies, insurance customers, and authorities with a reliable instrument for the insurance decision-making process. Moreover, because the world's insurance sector suffers from a gap of evaluation of private health insurance companies during the COVID-19 outbreak, the need for a reliable, useful, and comprehensive decision tool is obvious. Accordingly, this article aims to identify insurance companies' priority ranking in terms of healthcare services in Turkey during the COVID-19 outbreak through a multi-criteria performance evaluation methodology. Herein, alternatives are evaluated and then ranked as per 7 criteria and assessments of 5 experts. Experts' judgments and assessments are full of uncertainties. We propose a Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) technique under an intuitionistic fuzzy environment to rank insurance companies. The outcomes yielded ten insurance companies ranking in terms of healthcare services in the era of COVID-19. The payback period, premium price, and network are determined as the most crucial factors. Finally, a comprehensive sensitivity analysis is performed to verify the proposed methodology's stability and effectiveness. The introduced approach met the insurance assessment problem during the COVID-19 pandemic very satisfactory manner based on sensitivity analysis findings.

A New Group Decision-Making Technique under Picture Fuzzy Soft Expert Information.

As an extension of intuitionistic fuzzy sets, the theory of picture fuzzy sets not only deals with the degrees of rejection and acceptance but also considers the degree of refusal during a decision-making process; therefore, by incorporating this competency of picture fuzzy sets, the goal of this study is to propose a novel hybrid model called picture fuzzy soft expert sets by combining picture fuzzy sets with soft expert sets for dealing with uncertainties in different real-world group decision-making problems. The proposed hybrid model is a more generalized form of intuitionistic fuzzy soft expert sets. Some novel desirable properties of the proposed model, namely, subset, equality, complement, union and intersection, are investigated together with their corresponding examples. Two well-known operations AND and OR are also studied for the developed model. Further, a decision-making method supporting by an algorithmic format under the proposed approach is presented. Moreover, an illustrative application is provided for its better demonstration, which is subjected to the selection of a suitable company of virtual reality devices. Finally, a comparison of the initiated method is explored with some existing models, including intuitionistic fuzzy soft expert sets.

A Novel Algebraic Structure of (α,ß)-Complex Fuzzy Subgroups.

A complex fuzzy set is a vigorous framework to characterize novel machine learning algorithms. This set is more suitable and flexible compared to fuzzy sets, intuitionistic fuzzy sets, and bipolar fuzzy sets. On the aspects of complex fuzzy sets, we initiate the abstraction of (α,ß)-complex fuzzy sets and then define α,ß-complex fuzzy subgroups. Furthermore, we prove that every complex fuzzy subgroup is an (α,ß)-complex fuzzy subgroup and define (α,ß)-complex fuzzy normal subgroups of given group. We extend this ideology to define (α,ß)-complex fuzzy cosets and analyze some of their algebraic characteristics. Furthermore, we prove that (α,ß)-complex fuzzy normal subgroup is constant in the conjugate classes of group. We present an alternative conceptualization of (α,ß)-complex fuzzy normal subgroup in the sense of the commutator of groups. We establish the (α,ß)-complex fuzzy subgroup of the classical quotient group and show that the set of all (α,ß)-complex fuzzy cosets of this specific complex fuzzy normal subgroup form a group. Additionally, we expound the index of α,ß-complex fuzzy subgroups and investigate the (α,ß)-complex fuzzification of Lagrange's theorem analog to Lagrange' theorem of classical group theory.

Investigation of Cyber-Security and Cyber-Crimes in Oil and Gas Sectors Using the Innovative Structures of Complex Intuitionistic Fuzzy Relations.

Recently, there has been enormous development due to advancements in technology. Industries and enterprises are moving towards a digital system, and the oil and gas industries are no exception. There are several threats and risks in digital systems, which are controlled through cyber-security. For the first time in the theory of fuzzy sets, this research analyzes the relationships between cyber-security and cyber-crimes in the oil and gas sectors. The novel concepts of complex intuitionistic fuzzy relations (CIFRs) are introduced. Moreover, the types of CIFRs are defined and their properties are discussed. In addition, an application is presented that uses the Hasse diagram to make a decision regarding the most suitable cyber-security techniques to implement in an industry. Furthermore, the omnipotence of the proposed methods is explained by a comparative study.

Robust Aggregation Operators for Intuitionistic Fuzzy Hypersoft Set With Their Application to Solve MCDM Problem.

In this paper, we investigate the multi-criteria decision-making complications under intuitionistic fuzzy hypersoft set (IFHSS) information. The IFHSS is a proper extension of the intuitionistic fuzzy soft set (IFSS) which discusses the parametrization of multi-sub attributes of considered parameters, and accommodates more hesitation comparative to IFSS utilizing the multi sub-attributes of the considered parameters. The main objective of this research is to introduce operational laws for intuitionistic fuzzy hypersoft numbers (IFHSNs). Additionally, based on developed operational laws two aggregation operators (AOs), i.e., intuitionistic fuzzy hypersoft weighted average (IFHSWA) and intuitionistic fuzzy hypersoft weighted geometric (IFHSWG), operators have been presented with their fundamental properties. Furthermore, a decision-making approach has been established utilizing our developed aggregation operators (AOs). Through the established approach, a technique for solving decision-making (DM) complications is proposed to select sustainable suppliers in sustainable supply chain management (SSCM). Moreover, a numerical description is presented to ensure the validity and usability of the proposed technique in the DM process. The practicality, effectivity, and flexibility of the current approach are demonstrated through comparative analysis with the assistance of some prevailing studies.

Belief and Possibility Belief Interval-Valued N-Soft Set and Their Applications in Multi-Attribute Decision-Making Problems.

In this research article, we motivate and introduce the concept of possibility belief interval-valued N-soft sets. It has a great significance for enhancing the performance of decision-making procedures in many theories of uncertainty. The N-soft set theory is arising as an effective mathematical tool for dealing with precision and uncertainties more than the soft set theory. In this regard, we extend the concept of belief interval-valued soft set to possibility belief interval-valued N-soft set (by accumulating possibility and belief interval with N-soft set), and we also explain its practical calculations. To this objective, we defined related theoretical notions, for example, belief interval-valued N-soft set, possibility belief interval-valued N-soft set, their algebraic operations, and examined some of their fundamental properties. Furthermore, we developed two algorithms by using max-AND and min-OR operations of possibility belief interval-valued N-soft set for decision-making problems and also justify its applicability with numerical examples.

A study on offshore wind farm siting criteria using a novel interval-valued fuzzy-rough based Delphi method.

This study investigates the degree of importance of criteria affecting the optimal site selection of offshore wind farms. Firstly, forty two different influential criteria have been selected by reviewing the scientific literature on offshore wind farm site selection. Secondly, a survey has been conducted receiving a response from thirty four internationally renowned experts across seventeen countries. Each participant is asked to indicate the importance and relevance of each criterion based on their experience. Finally, the importance of each criterion for offshore wind farm site selection is determined using a novel Decision Making-Level Based Weight Assessment (LBWA) approach based on interval-valued fuzzy-rough numbers (IVFRN). The proposed method allows exploitation of the uncertainties and subjectivity that exist in the decision-making process. The results from this study improve our understanding of the importance and impact of each criterion which we believe would be invaluable for the future studies on the site selection of offshore wind farms.

Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors.

Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.

Optimal selection of healthcare waste treatment devices using fuzzy-rough approach.

The escalating volume of healthcare waste (HCW) generated by healthcare facilities poses a pressing challenge for all nations. Adequate management and disposal of this waste are imperative to mitigate its adverse impact on human lives, wildlife, and the environment. Addressing this issue in Bosnia and Herzegovina involves the establishment of a regional center dedicated to HCW management. In practice, there are various treatments available for HCW management. Therefore, it is necessary to determine the priority for procuring different treatments during the formation of this center. To assess these treatment devices, expert decision-making employed the fuzzy-rough approach. By leveraging extended sustainability criteria, experts initially evaluated the significance of these criteria and subsequently assessed the devices for HCW treatment. Employing the fuzzy-rough LMAW (Logarithm Methodology of Additive Weights), the study determined the importance of criteria, highlighting "Air emissions" and "Annual usage costs" as the most critical factors. Utilizing the fuzzy-rough CoCoSo (the Combined Compromise Solution) method, six devices employing incineration or sterilization for HCW treatment were ranked. The findings indicated that the "Rotary kiln" and "Steam disinfection" emerged as the most favorable devices for HCW treatment based on this research. This conclusion was validated through comparative and sensitivity analyses. This research contributes by proposing a solution to address Bosnia and Herzegovina's HCW challenge through the establishment of a regional center dedicated to HCW management.

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination on people's immunity vary across several factors. After the pandemic, real-life post-vaccination anomalies significantly impact women's health, access to medical treatments and medications, mental well-being, and daily physical activities. However, there has been scant investigation into the physical, psychological, social, and economic ramifications of vaccine effects on women in the post-pandemic era. Therefore, conducting a comprehensive risk assessment is crucial to safeguard women from the post-vaccination effects.To address this issue, the research encompasses complex bipolar spherical fuzzy ℵ-soft set, which has two-sided periodic ambiguous data due to its parametric properties as an adaptable ℵ-soft set and distinguishing criteria as a complex bipolar spherical fuzzy set. In addition, some fundamental operations and properties are presented in a complex bipolar spherical fuzzy ℵ-soft environment. Furthermore, the robust assessment of a real-world application demonstrate the efficacy of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to optimise the decision result. Finally, the provided decision-making approach is compared with existing techniques to illustrate their remarkable credibility and integrity.

Application of the full consistency method (FUCOM) - Cosine similarity framework in 5G infrastructure investment planning: An approach for telecommunication quality improvements.

In the rapidly evolving telecommunications landscape, the shift towards advanced communication technologies marks a critical milestone. This transition promises to revolutionize connectivity by enabling seamless data downloads, high-quality video streaming, and instant access to applications. However, adapting to these advanced technologies poses significant challenges for infrastructure expansion, requiring innovative investment and deployment strategies. These strategies aim not only to enhance service quality but also to ensure extensive network coverage. To address the need for systematic planning in infrastructure investment, this paper presents a novel methodology that combines the Full Consistency Method (FUCOM) with cosine similarity analysis. This integrated approach effectively prioritizes service areas for the deployment of 5G technology, emphasizing the importance of detailed planning in mobile strategy development. By leveraging FUCOM to determine the weights of various criteria and employing cosine similarity analysis to rank service areas, the methodology facilitates efficient resource allocation and service quality enhancements. Empirical validation using real data from a Turkish telecommunications company confirmed the effectiveness of the proposed algorithm. The results indicate that this integrated approach can significantly advance the telecommunications industry by providing essential insights for companies seeking to improve service quality amidst the transition to 5G and beyond. The successful implementation of the proposed algorithm demonstrates its effectiveness in addressing the challenges faced by telecommunications companies and underscores the importance of a data-driven approach in strategic decision-making and resource allocation. Furthermore, the findings suggest that the integrated FUCOM and cosine similarity analysis approach can offer a valuable tool for telecommunications companies worldwide, offering a systematic method for prioritizing infrastructure investments and enhancing network performance.

A modified EDAS model for comparison of mobile wallet service providers in India.

The present paper has two-fold purposes. First, the current work provides an integrated theoretical framework to compare popular mobile wallet service providers based on users' views in the Indian context. To this end, we propose a new grey correlation-based Picture Fuzzy-Evaluation based on Distance from Average Solution (GCPF-EDAS) framework for the comparative analysis. We integrate the fundamental framework of the Technology Acceptance Model and Unified theory of acceptance and use of technology vis-à-vis service quality dimensions for criteria selection. For comparative ranking, we conduct our analysis under uncertain environments using picture fuzzy numbers. We find that user-friendliness, a wide variety of use, and familiarity and awareness about the products help reduce the uncertainty factors and obtain positive impressions from the users. It is seen that PhonePe (A3), Google Pay (A2), Amazon Pay (A4) and PayTM (A1) hold top positions. For validation of the result, we first compare the ranking provided by our proposed model with that derived by using picture fuzzy score based extensions of EDAS and another widely used algorithm such as The Technique for Order of Preference by Similarity to Ideal Solution. We observe a significant consistency. We then carry out rank reversal test for GCPF-EDAS model. We notice that our proposed GCPF-EDAS model does not suffers from rank reversal phenomenon. To examine the stability in the result for further validation, we carry out the sensitivity analysis by varying the differentiating coefficient and exchanging the criteria weights. We find that our proposed method provides stable result for the present case study and performs better as ranking order does not get changed significantly with the changes in the given conditions.

Solar PV power plant site selection using a GIS-based non-linear multi-criteria optimization technique.

The ongoing rise in energy consumption imposed serious environmental challenges by using fossil fuels. The use of renewable energy sources is being increasingly explored as a potential answer for achieving sustainable energy production and minimizing adverse environmental effects. In the modern day, photovoltaic (PV) systems are viewed as a possible replacement for fossil fuels as a clean energy source. The installation of solar PV power plants requires vast land and huge investment. Therefore, it is necessary to select a suitable site to achieve maximum efficiency and low cost. A feasible location of photovoltaic (PV) system must consider certain criteria including land restrictions, access to roads, and transmission lines. This study analyzed ten factors grouped into four categories: geographic, technical, economic, and flood susceptibility criterion. The data of each factor is extracted from various governments, United Nation (UN), and non-government organizational bodies. Weights were assigned to ten factors by using a non-linear multi-criteria optimization technique called full consistency method (FUCOM). A geographic information system (GIS) software, ESRI ArcGIS pro, performs the weighted overlay analysis of the ten factors with weighted importance calculated by the above technique. A suitability map is created showing that a total of 2.02% of the country's area is suitable for PV power plants, which are further divided into five suitability classes. The results highlight the distribution of suitable sites for the construction of solar PV power plant throughout the country. A sensitivity analysis is performed to highlight the impact of the factor on the final suitability map. These findings can promote the future widespread development and application of solar energy resources.

Evaluation of the route selection in international freight transportation by using the CODAS technique based on interval-valued Atanassov intuitionistic sets.

The selection of a proper international freight transport route is one of the crucial tasks for decision-makers since it can affect costs, efficiency, and transportation performance. Besides, the selection of suitable and appropriate freight routes can also reduce external costs of transportation such as emissions, noise, traffic congestions, accidents, and so on. Route selection in international transportation is a complicated decision-making problem as many conflicting factors and criteria affect the assessment process. It has been observed that there is no mathematical model and methodological frame used for solving these selection problems, and decision-makers make decisions on this issue based on their own experiences and verbal judgments in the research process. Therefore, a methodological frame is required to make rational, realistic, and optimal decisions on route selection. From this perspective, the current paper proposes using the IVAIF CODAS, an extended version of the traditional CODAS techniques, and using the Atanassov interval-valued intuitionistic fuzzy sets (IVAIFS) for processing better the existing uncertainties. The proposed model is applied to solve the route selection, a real-life decision-making problem encountered in international transportation between EU countries and Turkey. According to the results of the analysis, option A6 (i.e., Route-6 (Bursa-Istanbul-Pendik-Trieste (Ro-Ro)-Austria-Frankfurt/Germany) has been determined as the best alternative. These obtained results have been approved by a comprehensive sensitivity analysis performed by using different MCDM techniques based on interval-valued intuitionistic fuzzy sets. Hence, it can be accepted that the proposed model is an applicable, robust, and powerful mathematical tool; also, it can provide very reliable, accurate, and reasonable results. As a result, the proposed model can provide a more flexible and effective decision-making environment as well as it can provide valuable advantages to the logistics and transport companies for carrying out practical, productive, and lower cost logistics operations.

Entropy and discrimination measures based q-rung orthopair fuzzy MULTIMOORA framework for selecting solid waste disposal method.

Fastest growing population, rapid urbanization, and growth in the disciplines of science and technology cause continually development in the amount and diversity of solid waste. In modern world, evaluation of an appropriate solid waste disposal method (SWDM) can be referred as multi-criteria decision-making (MCDM) problem due to involvement of several conflicting quantitative and qualitative sustainability indicators. The imprecision and ambiguity are usually arisen in SWDM assessment problem, and the q-rung orthopair fuzzy set (q-ROFS) has been recognized as one of the adaptable and valuable ways to tackle the complex uncertain information arisen in realistic problems. In the context of q-ROFSs, entropy is a significant measure for depicting fuzziness and uncertain information of q-ROFS and the discrimination measure is generally used to quantify the distance between two q-ROFSs by evaluating the amount of their discrimination. Thus, the aim of this study is to propose a novel integrated framework based on multi-attribute multi-objective optimization with the ratio analysis (MULTIMOORA) method with q-rung orthopair fuzzy information (q-ROFI). In this approach, an integrated weighting process is presented by combining objective and subjective weights of criteria with q-ROFI. Inspired by the q-rung orthopair fuzzy entropy and discrimination measure, objective weights of criteria are estimated by entropy and discrimination measure-based model. Whereas, the subjective weights are derived based on aggregation operator and the score function under q-ROFS environment. In this respect, novel entropy and discrimination measure are proposed for q-ROFSs. Furthermore, to display the feasibility and usefulness of the introduced approach, a case study related to SWD method selection is presented under q-ROFS perspective. Finally, comparison and sensitivity investigation are presented to confirm the robustness and solidity of the introduced approach.

Multi-attribute group decision-making based on Pythagorean fuzzy rough Aczel-Alsina aggregation operators and its applications to Medical diagnosis.

The fusion of information is a very hectic process whenever we analyze the information. Several frameworks have been introduced to reduce the uncertainty while fusing the information. Among those techniques, the Pythagorean fuzzy rough set (PyFRS), which is based on approximations is a key idea for dealing with uncertainty when data is taken from real-world circumstances. Furthermore, the most adaptable and flexible operational laws based on the parameters for fuzzy frameworks are Aczel-Alsina t-norm (AATNM) and Aczel-Alsina t-conorm (AATCNM). The major goal of this work is to introduce some methods for the basic operations of the information in the shape of Pythagorean fuzzy rough (PyFR) values (PyFRVs). Consequently, the PyFR Aczel-Alsina weighted geometric (PyFRAAWG), PyFR Aczel-Alsina ordered weighted geometric (PyFRAAOWG), and PyFR Aczel-Alsina hybrid weighted geometric (PyFRAAHWG) operators are developed in this article based on AATNM and AATCNM. Further, some basic properties of the developed operators are observed and discussed. Further, the developed approaches are applied to the problem of multi-attribute group decision-making (MAGDM). The obtained results from the MAGDM problem are observed at various values of the parameters involved by AATNM and AATCNM. Moreover, the results are also compared with already existing techniques for the significance of the developed approach.

Designing a fuzzy decision support framework for assessing solid waste management in the South European region.

The environmental planning of cities and rural regions is associated with monitoring the performance of several services, including solid waste management. This study proposes a new framework for the comparative assessment of the performance of integrated municipal solid waste management. The framework includes the fuzzy MACBETH multi-criteria decision-making model used to investigate the uncertainties and inefficiencies associated with solid waste management systems. The model consists of all major stages in solid waste management and its environmental impact. The applicability of the model was examined in the South European region. According to the global fuzzy values of the criteria weights, the most influential reported criteria were GHG emission (0.113,0.157,0.202), the waste generation growth rate (0.034,0.063,0.141), and waste generation (0.034,0.054,0.127). The other results indicated that Italy (47.26) and France (42.67) had shown a better performance, only to be followed by Spain (37.68), whereas Greece (15.77) and Portugal (12.85) had received the lowest score. In the context of promoting the circular economy in Europe, having a higher recycling rate and less landfilling is beneficial for Greece and Portugal. In addition to this, all these countries should make efforts on decoupling the waste generation-GDP correlation. Furthermore, the applicability of the model depends on an appropriate scale and criteria. The model can be replicated to other developed societies with a few modifications. However, it is necessary to modify the criteria for assessing developing societies based on local conditions.