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The age of industrialization and modernization has increased energy demands globally. Solar energy has been recognized as an inexhaustible source of energy and has been applied for desalination and electricity generation. Among different non-conventional energy resources, Solar Energy (SE) is one of the main contributors to the global energy system. A photovoltaic system (PS) is applied to produce SE using photovoltaic cells. The selection of a solar panel includes many intricate factors involving both subjective and quantifiable parameters; therefore, it can be regarded as a complex Multi-Criteria Decision-Making (MCDM) problem. As the uncertainty commonly occurs in the selection of an ideal solar panel, the theory of Pythagorean fuzzy sets has been proven as one of the flexible and superior tools to deal with the uncertainty and ambiguity that arise in real-life applications. The aim of the study is to present an MCDM framework for solving the Solar Panel Selection (SPS) problem within the Pythagorean fuzzy (PF) environment. For this, first, a new integrated method is proposed based on the Stepwise Weight Assessment Ratio Analysis (SWARA) and VlseKriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) approaches in the Pythagorean fuzzy sets (PFSs) context. In the proposed approach, subjective weights of the evaluation criteria are calculated by the SWARA method, and the preference order of alternatives is decided by the VIKOR method in the PF context. The criteria weights evaluated by this approach involve the imprecision of experts’ opinions, which makes them more comprehensible. The computational procedure of the proposed methodology is established through a case study of the SPS problem under PF environment, which proves the applicability and efficiency of the proposed method. Furthermore, this study performs sensitivity analysis to reveal the stability of the developed framework. This analysis signifies that the solar panel option R4 constantly secures its highest ranking despite how the parameter values vary. In addition, a comparative study is discussed to analyze the validity of the obtained result. The results show that the proposed approach is more efficient and applicable with previously developed methods in the PFS environment.

Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries’ sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries’ sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries’ sustainability performance.

To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.

Abstract The main objective of this study is to analyze and compare greenhouse gas emission profiles, dynamics thereof, and the climate change mitigation efforts of the major players in the global climate change arena, namely the G7 and BRICS countries that are accounting for more than 60% of the world greenhouse gas emissions as in 2017. Given that the energy sector is the major source of the greenhouse emissions in these countries, the framework of sustainable energy development indicators was applied to the comparative analysis. The analysis revealed the differences among the groups of countries in terms of greenhouse gas emission profiles and development trends of these profiles. The convergence of greenhouse gas emission profiles can be noticed between G7 and BRICS countries during 1990–2017 period. Greenhouse gas emissions per capita has decreased in G7 countries and increased in BRICS countries. Energy intensity, economic growth and carbon factor are the main drivers of greenhouse gas emissions per capita growth in both groups of countries. Carbon factor represents carbon intensity of a country's energy sector and is measured by greenhouse gas emissions per unit of total primary energy supply. The impact of carbon factor was least significant among the main drivers in both groups. Energy intensity was the major driver of decrease in greenhouse gas emissions per capita for both groups, however, the economic growth exceeded the contribution of energy intensity in BRICS countries and greenhouse gas emissions have increased in this group during investigated period. The comparative analysis of greenhouse gas emission profiles development trends and the main drivers of these trends in both groups of countries provides important insights in shaping future climate change mitigation policies and developing greenhouse gas emission reduction targets. Due to the high greenhouse gas profiles, low GHG reduction commitments and insufficient climate change mitigation the Perform, Achieve and Trade (PAT) scheme in India efforts in both groups there is danger of postponing implementation of Paris commitments and achieving the 1.5° target. The policies implemented in high ranked countries, like or Integrated Resources Planning in South Africa or measures under Energy efficiency directive in EU can be applied in low ranked countries by providing the substantial contribution to the country's climate targets.

This paper aims to provide a bibliometric review on the diverse decision approaches in uncertain contexts for clean energy system (CES) assessment. A total of 126 publications are analyzed. Previous reviews on CES have discussed several research questions on the decision methods and the applicability of evaluating CES, along with the factors associated with CESs. In the present study, we focus on the bibliometric aspect that attempts to address questions related to the prominence of authors, countries/regions that focus on the current theme, impact of journals, importance of articles in the research community, and so on. The window considered for the study is from 2018 to 2021, with the motive to extend the review process from the preceding works. A review model is presented to address the questions based on the literature evidence. The results infer that CESs are the most viable mode for sustainable development, and the use of decision approaches is apt for the assessment of CESs.

Abstract Nowadays, health-care waste management is a challenging issue for both the public and government sectors because it contains infectious, radioactive, or hazardous waste. Selecting a suitable method for health-care waste disposal is known as a complex decision-making problem due to the existence of several multiple criteria that may in conflict with each other. In this paper, we propose a novel method called evaluation based on distance from average solution framework based on parametric divergence measures with the context of intuitionistic fuzzy sets to evaluate and rank the health-care waste disposal alternative. To do this, first, novel parametric intuitionistic fuzzy divergence measures are developed, and various desired properties have also been examined. Second, the decision experts are evaluated, and the criteria weights are computed by developed parametric divergence measure method. Third, the preference order of the alternatives is illustrated by the developed framework. The proposed framework is based on the positive and negative distances from the average solution. Moreover, the health-care waste disposal alternative selection problem is considered to elucidate the applicability of the proposed framework. Four health-care waste disposal alternatives, including incineration, steam sterilization, microwave and landfill disposal, are considered in this study. The health-care waste disposal alternatives considered in this study include. The outcome illustrates that, the steam sterilization was the highest appraisal score (0.7025) and therefore, it should be chosen as the most suitable health-care waste disposal method in this study. Also, a comparison with existing approaches is shown to illustrate the validity and practicability of the developed framework.

Resource depletion, population growth and environmental problems force companies to collect their end of life (EOL) products for reuse, recycle and refurbishment through reverse supply chain management (RSCM). Success in collecting the EOL products through RSCM depends on the customers’ participation intention. The objectives of this study are: (1) To examine the important factors influencing customers’ attitude to participate in RSCM; (2) To examine the important factors influencing customers’ subjective norm to participate in RSCM; (3) To examine the main factors influencing customers’ perceived behavioral control to participate in RSCM; (4) To examine the influence of attitude, subjective norms and perceived behavioral control on customers’ participation intention in RSCM. The Decomposed Theory of Planned Behaviour (DTPB) has been chosen as the underpinning theory for this research. The research conducted employed the quantitative approach. Non-probability (convenience sampling) method was used to determine the sample and data was collected using questionnaires. Partial Least Squares-Structural Equation Modeling (PLS-SEM) technique was employed. A total of 800 questionnaires were distributed among customers of electronic products in Malaysia. Finally, the questionnaire was distributed among the customers in electronic retailer companies based on convenience sampling method. The empirical results confirm that consumers perception about the risk associated with EOL electronic products, consumers’ ecological knowledge and relative advantages associated with reuse, repair and recycling can influence the attitude of consumers to return the EOL products for reuse, repair and recycling to producer.

In recent years, the assessment of desirable renewable energy alternative has been an extremely important concern that could change the environment and economic growth. To tackle the circumstances, some authors have paid attention to selecting the desirable renewable energy option by employing the decision-making assessment and linguistic term sets. With a fast-growing interest in multi-criteria group decision-making (MCGDM) problems, researchers are tirelessly working towards new techniques for better decision-making. Decision makers (DMs) generally rate alternatives linguistically with different probabilities occurring for each term. Previous studies on linguistic decision-making have either ignored this idea or have used an only a single value for representing the weight of the linguistic term. Since expression of the complete probability distribution is hard and implicit hesitation exists, representation of weights of the linguistic terms using a single value becomes imprecise and unreasonable. To avoid this challenge, an interval-valued probabilistic linguistic term set (IVPLTS) is used, which is a generalization of (probabilistic linguistic term set) PLTS. Inspired by the usefulness of IVPLTS concept, we develop a decision framework for rational decision making. Initially, some operational laws and axioms are presented. Further, a novel aggregation operator known as interval-valued probabilistic linguistic simple weighted geometry (IVPLSWG) is developed for aggregating DMs’ preferences. Also, criteria weights are determined using the newly developed interval-valued probabilistic linguistic standard variance (IVPLSV) approach and alternatives are ranked using the extended VIKOR (VlseKriterijumskaOptimizacijaKompromisnoResenje) method under IVPLTS environment. Finally, a numerical example of renewable energy assessment is demonstrated to show the practicality of the developed decision framework. Also, the strengths and weaknesses of the developed decision framework are illustrated by comparison with existing ones.