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Trends of globalization very often cause the emergence of phenomena that asymmetrically affect the overall sustainability of the transport system. In order to predict certain situations and potentially be able to manage the transport system, it is necessary to manage risk situations and traffic safety in a timely manner. This study has conducted an investigation which implies defining the level of safety of a total of nine sections of two-lane roads. The main aim of the paper is to create a new multiphase model consisting of CRITIC (The CRiteria Importance Through Intercriteria Correlation), Fuzzy FUCOM (Full Consistency Method), DEA (Data Envelopment Analysis), and Fuzzy MARCOS (Measurement Alternatives and Ranking according to the COmpromise Solution) methods for determining the level of traffic safety on road sections under the conditions of uncertainty. In order for the created model to be adequately applied, eight parameters were created, and they were classified through four inputs and four outputs. To calculate the significance of the inputs, the CRITIC method based on the symmetric correlation matrix was used, and taking into account the nature of the outputs, the Fuzzy FUCOM method based on averaged values using the fuzzy Bonferroni Mean (BM) operator was applied to determine their weights. To determine the degree of safety, the DEA model was created. After that, the Fuzzy MARCOS method was used in order to determine the final ranking of the remaining five sections of the road network. Finally, the verification of results was performed through three phases of Sensitivity Analysis (SA).

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.

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 Renewable Energy Resources (RERs) are becoming increasingly significant for energy generation and will have vital for the future of humans, earth, and other living things. Due to the negative environmental impacts of fossil fuels, the authorities have taken RERs into account further. As a result, the present study extended a novel model using the Level Based Weight Assessment (LBWA) model based on in Interval Rough Number (IRN) extension of the Combinative Distance-Based Assessment (CODAS) method to cope with uncertain domain-based RER selection. A survey method using literature review and interview has been conducted to find and select appropriate perspectives and criteria; in this regard, the selected criteria were classified based on four groups, including socio-political, environmental, economic, and technical. Five sources of renewable energy, including solar energy, biomass energy, wind energy, hydropower energy, and geothermal energy, are evaluated in Turkey. The results of the extended IRN CODAS approach demonstrated that hydropower energy had the first rank among other sources, followed by solar energy, geothermal energy, biomass energy, and wind energy. In the application of the suggested model, the most suitable RER alternative is determined for Turkey. The results of this study showed the effectiveness, efficiency, and capability of the proposed methodology in the assessment of RERs.

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 Renewable energy should play a crucial role in increasing energy supplies and achieving the potential target of reducing 50% of CO2 emissions by 2050. The main objective of this study is to propose a neuro-fuzzy modelling entitled ensemble-Adaptive Neuro-Fuzzy Inference System (ANFIS) learning to predict and analyse the interrelationship between renewable energy consumption, economic growth, and CO2 emissions of G8+5 countries. This will help the governments and industry sectors to formulate energy policies and develop energy resources sustainably. The prediction method was constructed by extracting the fuzzy rules from the real-world dataset of World Development Indicators (WDI) and generalising the relationships of the inputs and output parameters for accurate prediction of CO2 emissions. The performance of the proposed method was evaluated, and the results show its efficiency in the prediction of CO2 emissions by incorporating the import indicators, including renewable energy consumption and economic growth. The U test of Sasabuchi–Lind–Mehlum (SLM) was conducted to identify the interrelationship results obtained from the ensemble ANFIS learning and the Environmental Kuznets Curve (EKC) hypothesis. The results of SLM test found an inverse U-shape condition among all countries except Brazil. The prediction of CO2 emissions trends using the soft computing approach (ensemble ANFIS) indicated that the consumption of renewable energy reduces CO2 emissions. The proposed soft computing method was found efficient in predicting CO2 emissions. It was in line with the foreseen targets of increasing the renewable energy generation and achieving the nationally determined contributions (NDCs) objectives.

The influence of the international energy market on different economic aspects has increased significantly, especially after an escalation in the financialization of the energy market. Likewise following this trend, the relationship between the international energy market and the US sectoral returns has developed significantly since the last decade. We examine the presence of a non-linear relationship between major US sectors and energy commodities from January 2007 to December 2018. To rightly justify the application of non-linear estimation technique, we perform Narayan and Popp (2010) test followed by the Wald test to identify asymmetries in the model. These asymmetries are more pronounced in the coal market as compared to oil and gas markets. The results of NARDL model, which is capable of capturing the non-linear and asymmetric relationship, highlight the presence of asymmetries between the energy commodities and the US sectoral returns. This relationship is pronounced more in the short-run for all sectors. Our cumulative coefficients for representing the contemporaneous relationship between energy commodities and US sectoral returns also highlight not only consistent asymmetric behaviour in the short-run but show collective unitary positive and negative impact of oil, gas and coal market. As a robustness measure, our application of dynamic multipliers suggests that the relationship between energy commodities and US sectors converge in the long-run after 20–30 weeks’ period. The results of our study entail valuable implications for policymakers and investors. ; This research is partly funded by the University of Economics Ho Chi Minh City, Vietnam.

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.