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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.

The COVID-19 pandemic has created unexpected demand for air cargo in terms of rapid mobility of critical basic needs. Air cargo carriers aim to maximize their profits by taking advantage of the current demand and using their limited capacity in the right place. At this point, some of the qualifications of the airports in the places where demand plays a crucial role in this decision of the carriers. Thus, evaluating the factors considered in the airport selection for air cargo carriers during the COVID-19 period is curious. This study proposes a triangular fuzzy Dombi-Bonferroni best-worst method (BWM) framework with vast flexibility to establish the priority preferences of factors considered in selecting airports. The fuzzy BWM model becomes a superior decision support system by combining the Bonferroni mean operator's ability to consider interrelationships between attributes and the flexibility of the Dombi operator. In this sense, we highlight eighteen criteria based on five airport aspects: location, physical features, performance, costs, and reputation. Findings reveal that the foremost aspects are location and costs, whereas the most crucial factors are airport charges and handling charges. The study suggests that airports should follow a low-price policy for airport-related charges without compromising their sustainability to have a share of the increasing number of air cargo flights, especially during the COVID-19 period, when airline passenger flights are decreased. The study is crucial in deciding the strategy and policy of air cargo carriers and airports during the pandemic period.

In this study, an integrated multi-criteria framework is developed to evaluate a healthcare sector which is one of the main infrastructures for any country. Healthcare sector plays a significant role in economic development and social sustainability of countries. To improve performance of healthcare sectors, it is essentially required to evaluate the healthcare systems based on their specific characteristics in order to resolve their performance related issues based on sustainable development principles under social aspect. For this purpose, the proposed integrated framework applies a novel hybrid weight determination model using best-worst method (BWM) and level based weight assessment (LBWA) to determine the weights of healthcare indicators and subsequently, combined compromise solution (CoCoSo) method is further applied to evaluate healthcare performances of several countries according to the pre-determined indicator weights. To show applicability of the proposed framework, a real time case study for seven countries in Eastern Europe is considered based on the data set of Organisation for Economic Co-operation and Development (OECD). Results show that Lithuania and Slovakia have the best healthcare systems in comparison to countries like Poland and Estonia.

Cryptocurrencies have brought many innovations and discussions to economic life. Digital assets, which are very popular by investors, are frequently used for many purposes such as store of value, exchange, and speculation. It creates a research area that intentions cryptocurrency experts prioritize in crypto investments. In this paper, therefore, the fuzzy Full Consistency Method-Bonferroni (FUCOM-F’B) model is conducted to determine the priorities of drivers for investing in cryptocurrencies. The selected twenty-three drivers are classified based on five aspects, including functionality, financial, legal infrastructure, technology, and security. Based on the findings, “strong electronic encryption” and “use of digital signature” are the most significant drivers for preferring a cryptocurrency. A validation check is performed to verify the reliability, usefulness, and stability of the proposed approach. Further, the introduced approach allows taking the ambiguities and subjectivity into account which exist in the decision-making procedure. The suggested framework can be a helpful decision support tool for regulators, policymakers, practitioners, and cryptocurrency investors.

Greenhouse gas (GHG) emissions are one of the biggest challenging environmental problems globally, which leads countries to reduce their environmental impact in various disciplines. One of the most negative effects on the environment can be seen in the transportation area. It has been seen as a promising way to reduce emissions from transport with various alternative fuel vehicles (AFVs). This study aims to develop a multi-criteria decision-making (MCDM) methodology to prioritize the various AFVs for sustainable transport. The assessment of AFVs can be considered an MCDM problem due to the involvement of several conflicting criteria. We thus develop a novel multi-criteria decision-making methodology based on fuzzy Full Consistency Method (FUCOM-F) and neutrosophic fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (MARCOS) framework for the assessment of the AFVs. The proposed methodology is applied to prioritize the various AFVs in New Jersey, U.S. According to the findings, the most significant drivers for AFV selection are purchase cost, energy cost, and social benefits, respectively. The evaluation results also show that electric vehicles can serve as an effective approach to reducing carbon emissions for New Jersey. In addition, a comparative analysis is conducted to indicate the out-performance of the proposed multi-criteria methodology.

Social sustainability is considered as the least defined and addressed pillar among the three pillars of sustainability. Social sustainability goals and targets within sustainable development goals are currently considered vital factors for human beings. Therefore, countries continuously evaluate their performance with respect to achieving social sustainability goals to promote required commitments and partnerships to address existing social problems and to maximize social satisfaction of their citizens. In this regard, this paper evaluates the social sustainability performance in seven developed countries that are included in the Group of Seven (G7) as one of the most important intergovernmental economic organizations. Assessing the performance of the countries is conducted based on real data from Organisation for Economic Co-operation and Development (OECD) dataset, where fourteen indicators are selected from different aspects that have noticeable roles on social sustainability. Evaluation of countries is done through a novel integrated data-driven weighting system based on CRITIC and Shannon’s Entropy methods, and CoCoSo method. Using the integrated data-driven weighting system, the weight of indicators is determined by using an aggregation operator to combine weights obtained from CRITIC and Shannon’s Entropy. The proposed integrated data-driven weighting system is designed to remove the biasedness and subjectivity of experts’ opinions that can happen using other weighting methods. Then, countries are comparatively evaluated and ranked using the CoCoSo. Based on the results of the proposed model, France shows the best performance with respect to social sustainability indicators.

Due to the ever-increasing harmful emissions affecting natural life and health seriously, it is inevitable the usage of renewable energy sources instead of fossil resources in the near future. Another drawback of fossil fuels is several threats like environmental pollution and global warming, which are potential risks for future generations. Given that the transportation sector makes a huge contribution to carbon emissions, the importance of battery electric vehicles (BEVs), which are an eco-friendly form of vehicles is obvious. Because the BEV market has been rapidly expanding recently, it has become a significant issue to assess BEV alternatives comprehensively from the customer's point of view. This assessment can be made by addressing the basic features of each BEV. Further, multiple criteria decision making (MCDM) techniques are efficient instruments for the right BEV purchase decision. In this work, therefore, ten BEVs are chosen as alternatives. These vehicles are then ranked using SECA, MARCOS, MAIRCA, COCOSO, ARAS, and COPRAS multi-criteria techniques on the basis of technical specifications, such as acceleration, price, battery, range, and so on. Afterward, results from various MCDM techniques are aggregated by applying the Borda count and Copeland ranking methodologies. “Price”, “permitted load,” and “energy consumption” are determined as the most three significant factors for BEV selection, respectively, whereas Tesla Model S is highlighted as the best choice. Further, the robustness and reliability of the results are performed by applying a sensitivity analysis. The proposed framework can be utilized as a basis for more detailed purchasing decisions.

In sustainable supply chain management (SSCM), triple bottom line (TBL) of sustainability (economic, social, and environmental) are considered when selecting the suppliers. In this paper, the relative weights of SSCM practices are extracted by fuzzy best worst method (F-BWM) which is capable of better modeling of human thinking. Afterwards, the traditional Combined Compromise Solution (CoCoSo) method is enhanced by the integration of the normalized weighted and the normalized weighted geometric Bonferroni mean functions to select the most proper supplier in a supply chain. The integration of the Bonferroni functions into the CoCoSo method (CoCoSo’B) enables: (1) flexible decision-making respecting the interaction between decision attributes; (2) the elimination of the influence of extreme/awkward data on the values of the criterion functions, and (3) checking the robustness of the results through a variation of the parameters λ, p, and q. Testing the model on the example of sustainable supplier selection demonstrates the rationality, objectivity, flexibility, and stability of the results of the proposed model. Further, the proposed framework makes it possible to evaluate suppliers in terms of sustainability in spite of ambiguities in the decision-making process and a lack of quantitative information. Finally, a real world example of home appliance manufacturer in Serbia is discussed to verify the proposed model applicability.

Sustainable development (SD) can be considered as a bridge incorporating economic development and environmental protection. Sustainability in oil industry is a crucial environmental issue, from the extraction to the delivery levels. As a result of the rapidly increasing population and industrialization, there would not only be the energy demand but also an increase in oil production in next decades affecting SD. Unfortunately, some industries including oil production are directly related to the CO2 and other harmful emissions making it an important issue in terms of sustainability. Therefore, assessment of sustainability performance of OPEC countries is of vital importance affecting the global energy sectors hierarchies. Along this line, via using a multiple attribute decision making (MADM) approach namely Combined Compromise Solution (CoCoSo), the OPEC countries are analysed according to 41 SD indicators in 10 dimensions in this study. Twelve selected main members of OPEC are evaluated based on the official real data and the outputs are tested through sensitivity analyses compared to other extensively known robust MADM methods. Based on the findings, United Arab Emirates is the most sustainable OPEC country with 71.9% performance score as well as Qatar, Kuwait, and Iran are positioned next with 69.3%, 66.6, and 56.2% performance scores, respectively. High correlation (greater than 97%) of the method used in this study compared to other robust MADM methods (WASPAS, MABAC, CODAS, and VIKOR) demonstrated the effectiveness and usefulness of the proposed method.