An Extended GRA Method Integrated with Fuzzy AHP to Construct a Multidimensional Index for Ranking Overall Energy Sustainability Performances

Integrated analytical hierarchy process-grey relational analysis approach for mechanical recycling scenarios of plastics waste in India

Mechanical recycling is an indispensable tool for plastic waste (PW) recycling and has the highest share in the PW recycling sector in India. The transition to the circular economy of plastics (CEoP) needs a systemic perspective on the mechanical recycling processes. Nevertheless, the assessment of multiple parameters influencing the mechanical recycling of PW is a complex decision-making problem for the development of triple-bottom-line mechanical recycling. A systemic perspective of various mechanical recycling scenarios was performed by employing a multi-criteria decision-making approach to examine the complexity of interlinked factors in the present investigation. Analytical hierarchy process (AHP) integrated with grey relational analysis (GRA) was used to evaluate the criteria that directly influence quality-oriented mechanical recycling. Data were collected by conducting semi-structured interviews using a framed questionnaire in stakeholder engagement with mechanical recyclers of PW. The first level hierarchy included economy, technical, resource consumption and environmental criteria. These criteria were further categorized into various significant indices such as quality of recyclate, recyclability, water and energy consumption during recycling. The results of the integrated grey relational analysis indicated that the technical parameters including quality of recyclate, resource efficiency, PW processing rate and recyclability have a significant influence on mechanical recycling. Based on AHP-GRA, scenario MR6, i.e. manufacturing of PET strap from recycled PET flakes, was ranked the optimal mechanical process amongst the various scenarios. MR6 was followed by Straps and Films at the second and third rank. The lowest ranking was observed for polymer blend recycling. These processes with higher ranks produced good quality recyclate with better efficiency and recyclability. Moreover, these processes consumed optimal resources during manufacturing. These processes also exhibited less maintenance cost, high production rate, low chemical consumption and waste generation as well as implemented pollution control practices.

The development of a grey relational analysis-based composite index for environmental sustainability assessment: Towards a net-zero emissions strategy in Saudi Arabia

Climate change and environmental sustainability assessment are essential in city planning, design, and smart city advancement. Despite Saudi Arabia's high global greenhouse gas (GHG) emissions ranking, a comprehensive review of extant studies revealed insufficient tools enhancing the policymaking and comprehension of climate change and environmental performance. This paper developed a hybrid green city index (GCI) and grey relational analysis (GRA) composite index for appraising national environmental sustainability via a robust, efficient, effective, and replicable grading process. The index is designed based on two primary considerations. The first is the selection of quality underlying indicators/categories, while the second is the adoption of GRA for conducting the normalization, weighting and aggregation process. These two considerations influenced the proposed composite index, which was later applied to Saudi Arabia as a study area. The results revealed that the environmental sustainability of Saudi Arabia is not significant, with the most outstanding of 0.3127 for 2010. At the category level, the favourable environmental sustainability ranking is between the 2010 and 2012 assessment period, with a gradual decline till 2018. This study's findings are unique as no studies within the context of Saudi Arabia and the Gulf region have utilized this study's research approach. Although not all indicators of the proposed index were used in the study area, this study's methodology and outcomes have the beneficial impact of assisting Saudi Arabia's decision-makers across the cities in monitoring the status and progress of implementing its net zero carbon emissions by 2060.

Research on the Prediction Model of the Used Car Price in View of the PSO-GRA-BP Neural Network

As the mobile Internet improves by leaps and bounds, the model of traditional offline used car trading has gradually lost the ability to live up to the needs of consumers, and online used car trading platforms have emerged as the times require. Second-hand car price assessment is the premise of second-hand car trading, and a reasonable price can reflect the objective, fair, and true nature of the second-hand car market. In order to standardize the evaluation standards of used car prices and improve the accuracy of used car price forecasts, the linear correlation between vehicle parameters, vehicle conditions, and transaction factors and used car price was comprehensively investigated, grey relational analysis was applied to filter the feature variables of factors affecting used car price, the traditional BP neural network was also optimized by combining the particle swarm optimization algorithm, and a used car price prediction method based on PSO-GRA-BPNN was proposed. The results show that only the correlation coefficient of new car price, engine power, and used car price is greater than 0.6, which has a certain linear correlation. The correlation between new car price, displacement, mileage, gearbox type, fuel consumption, and registration time on used car prices is greater than 0.7, and the impact of other indicators on used car prices is negligible. Compared with the traditional BPNN model and the multiple linear regression, random forest, and support vector machine regression models proposed by other researchers, the MAPE of the PSO-GRA-BPNN model proposed in this paper is 3.936%, which is 30.041% smaller than the error of the other three models. The MAE of the PSO-GRA-BPNN model is 0.475, which is a maximum reduction of 0.622 compared to the other three models. R can reach up to 0.998, and R2 can reach 0.984. Although the longest training time is 94.153 s, the overall prediction effect is significantly better than other used car price prediction models, providing a new idea and method for used car evaluation.

Objective Criticism and Negative Conclusions on Using the Fuzzy SWARA Method in Multi-Criteria Decision Making

The quality of output or decision-making depends on high-quality input data, their adequate evaluation, the application of adequate approaches, and accurate calculation. In this paper, an objective criticism of applying the fuzzy SWARA (step-wise weight assessment ratio analysis) method based on the Chang TFN (triangular fuzzy number) scale is performed. Through research, it has been noticed that a large number of studies use this approach and, as an epilogue, there are wrong decisions based on inconsistent values in relation to the initial assessment of decision-makers (DMs). Seven representative studies (logistics, construction industry, financial performance management, and supply chain) with different parameter structures and decision matrix sizes have been singled out. The main hypothesis has been set, which implies that the application of this approach leads to wrong decisions because the weight values of the criteria are incorrect. A comparative analysis with the improved fuzzy SWARA (IMF SWARA) method has been created and a number of negative conclusions has been reached on using the fuzzy SWARA method and the Chang scale: Primarily, that using such an approach is impossible for two or more criteria to have equal value, that allocating TFN (1,1,1) leads to criteria values that are inconsistent with expert evaluation, that the last-ranked criteria in the fuzzy SWARA method have no influential value on the ranking of alternatives, that there is a great gap between the most significant and last-ranked criteria, and that the most significant criterion has a huge impact on the evaluation of alternative solutions and decision making. As a general conclusion, it is given that this approach is not adequate for application in problems of multi-criteria decision making because it produces inadequate management of processes and activities in various spheres.

Assessment of Energy Sustainability Issues in the Andean Community: Additional Indicators and Their Interpretation

To achieve sustainable development goals (SDGs), it is necessary to solve the problem of assessing and measuring energy sustainability performance. A popular indicator used for this purpose is the World Energy Council (WEC) energy sustainability index, or the Energy Trilemma Index, which is based on such key metrics as energy security, energy equity, environmental sustainability, and country context. Each of the four metrics, or dimensions, includes many indicators that depend on both internal and external factors. By combining a variety of indicators into integral ones, WEC ranks countries in terms of energy sustainability. However, what is not taken into account is how countries differ in terms of economic development, income, energy mix, renewables use, ownership in the energy sector, and other factors, and neither is the methodology itself disclosed by the developers. As a provider for all other sectors of the economy, the energy sector plays an important role in developing countries. Ecuador, Colombia, Peru, and Bolivia, being members of the Andean Community, are neighbors and have similar economic conditions but lack transnational power grid interconnections, which hinders the development of a common energy market. In terms of energy sustainability, these countries’ ranks range from 45 to 101, according to the Energy Trilemma Index. The aim of the study is to develop a new methodology for assessing energy sustainability performance that will factor in the specific features of developing countries with a high share of hydroelectricity generation, and to calculate energy sustainability index indicators taking into account contemporary requirements for sustainable development, which include developing green and renewable energy and fostering decarbonization. This research reveals whether the countries’ energy sustainability indices correspond to their actual performance in energy development and identifies the factors influencing the values of the metrics in the Energy Trilemma Index. The methodology can be used to integrate the energy sectors of countries, as it allows for evaluating the state of the energy sector of several countries (for example, those of the Andean Community) as a whole.

Comparative Analysis of Five Widely-Used Multi-Criteria Decision-Making Methods to Evaluate Clean Energy Technologies: A Case Study

Over the last decade, the total primary energy consumption has increased from 479 × 1015 BTU in 2010 to 528 × 1015 BTU in 2020. To address this ever-increasing energy demand, as well as prevent environmental pollution, clean energies are presented as a potential solution. In this regard, evaluating and selecting the most appropriate clean energy solution for a specific area is of particular importance. Therefore, in this study, a comparative analysis in Jiangsu province in China was performed by describing and implementing five prominent multi-criteria decision-making methods in the field of energy technology selection, including SAW, TOPSIS, ELECTRE, VIKOR, and COPRAS. The decision problem here consists of four clean energy options, including solar photovoltaic, wind, nuclear, and biomass, which have been evaluated by twelve basic and important criteria for ranking clean energy options. The obtained results, according to all five MCDM methods, indicate that solar photovoltaic was the optimal option in this study, followed by wind energy. The nuclear and biomass options placed third and fourth, respectively, except in the ELECTRE method ranking, in which both options scored the same and thus neither was superior. Finally, by conducting a comprehensive two-stage sensitivity analysis, in the first stage, it was found that changes in the weights of land use and water consumption criteria had the greatest impact on the performance of options, among which biomass and nuclear showed high sensitivity to variations in criteria weights. In the second stage, by defining five scenarios, the ranking of options was evaluated from different aspects so that the decision maker/organization would be able to make appropriate decisions in different situations.

Selection of the Multiple-Criiater Decision-Making Method for Evaluation of Sustainable Energy Development: A Case Study of Poland

One of the basic objectives of the European Union’s energy policy is to obtain and use energy in a sustainable way. Multiple-criteria decision making (MCDM) methods, in particular linear ordering based on the synthetic variable procedure, are used for comparative analyses of the level of energy sustainability. Despite many studies, the problem of choosing the optimal ordering method is still not fully resolved. This paper presents an original procedure that facilitates the selection of an effective method of the linear ordering of multi-feature objects for the evaluation of sustainable energy development of regions. What is understood as the effective ordering of regions is not only the effective ranking of objects but also their effective clustering. In order to obtain the best results of linear ordering, the authors put forward a multi-stage optimization of the selection of the method of ordering and normalization of diagnostic variables. Analysis of variance was used for the assessment of the object ranking quality, while for the assessment of the object clustering quality, an innovative approach was presented based on the analysis of the empirical distribution of the frequency of occurrence of the distance between objects. The linear ordering method, selected on the basis of the procedure proposed in the paper, was used to assess the energy sustainability of Polish regions. The calculations and analyses were carried out using the set of indicators developed by the authors. They characterize the sustainable energy development of regions in the social, economic, and environmental dimensions.

Using a Fuzzy Analytic Hierarchy Process to Formulate an Effectual Tea Assessment System

Taiwan tea is very famous around the world. This study aims to establish a quantized tea assessment system to increase the credibility of the current tea evaluation mechanism. In this study, a new procedure using a fuzzy analytic hierarchy process integrated with linguistic variables is proposed to set up measurable indicators and determine their weights for a tea evaluation mechanism. An affinity diagram was used to deduce three dimensions (i.e., tea farm management, the tea-making process, and organoleptic evaluation) and 11 evaluation criteria for the construction of the tea assessment system. Sixteen experts, including 10 senior tea farmers and six national tea appraisers, were invited to participate in the one-on-one linguistic questionnaire survey. Analysis of the fuzzy analytic hierarchy process shows that tea farm management gains the most weight (0.533), followed by the tea-making process (0.329), and organoleptic evaluation (0.138). Surprisingly, organoleptic evaluation, as the assessment criteria today, places last in the three evaluation dimensions. Findings of this study can provide the Taiwan Tea Association with insightful information for enhancing the current tea assessment system.

Supportiveness of Low-Carbon Energy Technology Policy Using Fuzzy Multicriteria Decision-Making Methodologies

The deployment of low-carbon energy (LCE) technologies and management of installations represents an imperative to face climate change. LCE planning is an interminable process affected by a multitude of social, economic, environmental, and health factors. A major challenge for policy makers is to select a future clean energy strategy that maximizes sustainability. Thus, policy formulation and evaluation need to be addressed in an analytical manner including multidisciplinary knowledge emanating from diverse social stakeholders. In the current work, a comparative analysis of LCE planning is provided, evaluating different multicriteria decision-making (MCDM) methodologies. Initially, by applying strengths, weaknesses, opportunities, and threats (SWOT) analysis, the available energy alternative technologies are prioritized. A variety of stakeholders is surveyed for that reason. To deal with the ambiguity that occurred in their judgements, fuzzy goal programming (FGP) is used for the translation into fuzzy numbers. Then, the stochastic fuzzy analytic hierarchical process (SF-AHP) and fuzzy technique for order performance by similarity to ideal solution (F-TOPSIS) are applied to evaluate a repertoire of energy alternative forms including biofuel, solar, hydro, and wind power. The methodologies are estimated based on the same set of tangible and intangible criteria for the case study of Thessaly Region, Greece. The application of FGP ranked the four energy types in terms of feasibility and positioned solar-generated energy as first, with a membership function of 0.99. Among the criteria repertoire used by the stakeholders, the SF-AHP evaluated all the criteria categories separately and selected the most significant category representative. Finally, F-TOPSIS assessed these criteria ordering the energy forms, in terms of descending order of ideal solution, as follows: solar, biofuel, hydro, and wind.

Exploring Grey Systems Theory-Based Methods and Applications in Sustainability Studies: A Systematic Review Approach

In recent years, there have been international movements advocating more sustainable societies, and as a result of such movements, a remarkably important sub-branch has been shaped in systems studies called sustainability. It would be vital to propose methods that could deal with inherent complexities and uncertainties in such systems. Grey systems theory (GST) represents a nascent method that could help to solve complexities in the face of multifaceted problems, uncertainty, and complexity in systems, and the theory could considerably contribute to sustainability studies. The present study sought to fill a gap and provide an updated review of the literature on the roles and impacts of GST-based methods in sustainability studies as one of the most significant areas of exploring economic, social and environmental systems. Primarily, the theoretical foundations of sustainability and GST were briefly reviewed. Next, by categorizing the studies conducted in the literature on sustainability studies, GST-based methods used in such studies were identified. Finally, the advantages, effects and functions of GST-based theories and their applications in sustainability studies were explicated. The papers found in this systematic review were searched on such databases as Scopus, Web of Science, and ScienceDirect, as published from 2010 up to the first three months of 2020, based on these keywords: grey relation or grey relational, grey model, grey system or grey systems, grey prediction, grey control, grey incidence, grey cluster, grey decision, grey input-output. The total number of publications found on all of the databases was 446, although (following a more meticulous investigation of the publications) 145 ones were used for the comprehensive analysis. The 10 different areas in which GST was used to explore sustainability in the publications were: sustainability assessment, industrial sustainability, urban sustainability, energy sustainability, sustainability development, businesses sustainability, agricultural sustainability, sustainable products, tourism sustainability, social sustainability. The results revealed that complexity, uncertainty, and inaccessibility of a large set of data and initial statistical distributions led researchers to rely on GST in sustainability studies, and that the applied areas of GST in terms of sustainability issues had some features in common, including linguistic variables, long-term projects, technological demands, conflicting goals, and uncertainty. Moreover, compared to other methods used to deal with uncertainty, GST did not require the formation of an extensive databank of classified rules and was more practical and efficient in sustainability calculations (as complex systems) with fewer numerical calculations. Ignoring systematic approaches, causal relations, cause-effect loops, and dynamic feedback was the missing link in the application of GST in sustainability studies as complex economic, social and environmental systems.