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Since the demand for energy has dramatically increased in the countries which have fast-growing population and economy, they have faced with a critical problem of how to evaluate a set of potential energy sources (i.e. nuclear, natural gas, bio, geothermal, hydro, wind and solar) and choose the ultimate energy source for their needs. On the other hand, this critical problem turns into a multiple-criteria decision-making (MCDM) in the presence of a set of energy source alternatives and evaluation criteria. In literature, there are many MCDM methods introduced to solve for different kinds of problems. The purpose of this paper is to present an integrated approach for evaluating energy sources using fuzzy AHP and GRA, with a case for Turkey.

In this paper, the analytic hierarchy process (AHP) and grey relational analysis (GRA) methods are used because of their advantages for similar problems. On the other hand, due to the fact that the conventional AHP by a nine-point scale and GRA method using a scale with crisp values can be unable to handle to capture the right judgments of a decision-maker(s), to reflect the vagueness and uncertainty on the judgments of a decision-maker, the fuzzy logic is integrated with the AHP and GRA.

The contributions of the paper to the literature are given in two dimensions as follows: it presents an integrated approach for complex decision processes with subjective data or vague information; the proposed approach, the fuzzy AHP-GRA method for energy source selection, is unique for the related problem in literature. The results of the proposed model from the case of Turkey will help practitioners and experts of how to apply it to the similar problems in the field of energy management.

In short, in this paper, an integrated approach is proposed through the fuzzy AHP and the fuzzy GRA methods. As the fuzzy AHP is used to determine the weights of evaluation criteria, the fuzzy GRA is used to rank energy source alternatives.

In addition, a case study for Turkey is presented to show the applicability of the proposed approach for potential practitioners who are authority in the field of energy in public and private sectors.

On the other hand, the proposed approach, the fuzzy AHP-GRA for energy source selection can also be an intelligent tool for public and private energy companies in Turkey, as well as others in the world.

On the other hand, in this paper, to the best of the authors’ knowledge, the study contributes to the literature that the first time, they use the fuzzy alpha-cut AHP and GRA in fuzzy environment for energy source evaluation problem.

The purpose of this paper is to develop an integrated fuzzy multi-criteria decision-making (MCDM) model for evaluation of the energy alternates in India based on their sustainability.

A fuzzy analytical hierarchy process approach is used for the weight calculation of the criteria and the fuzzy technique for order preference by similarity to the ideal solution is used for ranking of the energy alternates. Seven energy sources – thermal, gas power, nuclear, solar, wind, biomass and hydro energy are considered for the assessment purpose on the basis of sustainability criteria, namely, economic, technical, social, environmental, political and flexible.

The result of the analysis shows that economics is the highest weight criterion, followed by environmental and technical criteria. Solar energy was chosen as the most sustainable energy alternate in India, followed by wind and hydro energy.

Few other MCDM techniques such as VIseKriterijumska Optimizacija I Kompromisno Resenje (multi-criteria optimization and compromise solution), weighted sum method and preference ranking organization method for enrichment evaluations – II can also be explored for the sustainability ranking of the energy alternates. However, the present model has also provided a good result.

The present research work will help the decision-makers and organizations in the evaluation and prioritizing the various energy sources on the scale of sustainability.

Research finding provides guidance to government and decision-makers regarding the development of social conditions through energy security, job creation and economic benefits.

Research work can be act as a supplement for the investors and decision-makers specifically in prioritizing the investment perspective and to support other multi-perspective decision-making problems.

This study aims to identify sustainability indicators in the energy sector through a literature review and validate them from the perspective and context of professionals working in the sector in an emerging economy country, Brazil, considering the relationship of these indicators with the achievement of the targets set by the United Nations sustainable development goals (UN SDGs).

To accomplish this, a literature review on sustainability indicators specific to the energy sector was conducted. Subsequently, a research instrument (questionnaire) based on the identified indicators was developed and a survey was administered to professionals in the field. The collected data were analyzed using the Lawshe method.

The results revealed 20 indicators, distributed across environmental, economic and social dimensions. Among these, nine indicators were validated, including global impacts, local impacts, renewable energy production as a percentage of total production, greenhouse gas emissions, access to electricity, investment in the energy sector, installed capacity in the electricity sector, energy prices in the end-use sector and energy distribution and conversion efficiency.

Consequently, it was possible to determine which SDGs are directly impacted and provide a foundation for future actions that can contribute to the sustainable advancement of the energy sector in emerging countries.

South Asia is one of the fastest-growing regions in the world. With its fast economic development, the energy requirement for the region has rapidly grown. As the region relies mainly on nonrenewable energy sources and is suffering from issues like pollution, the high cost of energy imports, depleting foreign reserves, etc. it is searching for those factors that can help enhance the renewable energy generation for the region. Thus, taking these issues into consideration, this paper aims to investigate the impact of macroeconomic factors that can contribute to the enhancement of renewable energy output in South Asia.

An autoregressive distributed lag methodology has been applied to examine the long-term effects of remittance inflows, literacy rate, energy imports, government expenditures and urban population growth on the renewable energy output of South Asia by using time series data from 1990 to 2021.

The findings indicated that remittance inflows have a negative and insignificant long-term effect on renewable electricity output. While it was discovered that energy imports, government spending and urban population growth have negative but significant effects on renewable electricity output, literacy rates have positive and significant effects.

Considering the importance of renewable energy, this is one of the few studies that has included critical macroeconomic variables that can affect renewable energy output for the region. The findings contribute to the body of knowledge that a high literacy level is crucial for promoting renewable energy output, while governments and policymakers should prioritize reducing energy imports and ensuring that government expenditures on renewable energy output are properly used. SAARC, the governing body of the region, also benefits from this study while devising the renewable energy output policies for the region.

In today's competitive environment, sustainability is talked out in every sphere of life. Sustainability is a key to stability and for that roots are being focused by incorporating sustainability in higher education. The basic purpose of this paper is to prioritize the sustainability drivers in the higher education system. This research will provide fruitful insight into the sustainability drivers in the higher education system to the education industry and policymakers.

The present research is conducted on the 400 students studying in four major universities in the state of Punjab. Fuzzy analytical hierarchy process was applied to prioritize the sustainability drivers in the higher education system. The primary factors considered for the present study include social-people (social responsibility), environmental-planet (sustainable environmental practices) and financial-profit (economic value created).

The most influential criteria were environmental-planet (sustainable environmental practices) and social-people (social responsibility). The five most influential subcriteria were “Student engagement in eco co-curricular activities (C21)”, Energy efficiency measures (C23)”, “The HEI as a job driver in the city (C11)”, “Total direct energy consumption (C31)” and “Support from the HEI for local initiatives and help in growing the sustainability of the community or region (C12)”.

Although the sample survey conducted in this study was focused on a small sample selected from the state of Punjab which genuinely represented the total population, it is still considered as a limitation for the present study.

The outcome of this research provides policymakers with a better understanding of the sustainability drivers in higher education. This will further help them toward achieving the aim of sustainability.

The present research is based on the available literature on sustainability and the results of the study would add value to the existing knowledge base.

The warehouse industry is one of the backbones in the logistics operation which involves several activities i.e. storage, receiving, picking and shipping of goods/cargoes. This study analyzes the most important warehouse productivity indicators for improving warehouse operation efficiency.

This study presents an empirical methodology of the fuzzy analytical hierarchy process (FAHP) method, an integration between the fuzzy logic method with an analytical hierarchy process (AHP) method incorporated with the adoption of quantitative and systems theories under the modern management theory approach.

The results indicate that the weight values of the main criteria which lead by the criterion “Space (0.4005)” at the top ranking, followed by Information System (0.2445), Labor (0.2065) and Equipment (0.1484). In addition, the weight values and ranking of the 16 sub-criteria are also highlighted which the sub-criterion “Warehouse Management System (0.2445)” scores the highest weight value and followed by Storage Space Utilization (0.1043) and Throughput (0.0722) accordingly.

Finally, this research contributed to enrich the literature, while highlighting a series of recommendations on the top three most significant productivity performance indicators that can be useful in further research.

A generic analysis model developed with the adoption of three study theories: quantitative, system and productivity theories.

Environmental crisis and energy security concerns forced researchers, environmentalists and industrialists to look for a cleaner mode of transportation. Rigorous efforts have been made to make electric vehicles (EVs) feasible for commercial use. However, despite of many efforts by the Government of India, the rate of adoption of EVs in India has not been up to the mark. To bridge this gap, present study understands the social acceptability and sustainability of EVs and identifies the social factors, builds inferences from the results obtained and helps in orienting the manufacturers and decision makers towards faster adoption of the EVs.

The social factors responsible for the growth of EVs in India are identified by literature survey. A questionnaire has been developed for understanding the customer’s perception towards EVs. The results of the survey are analysed using the tools on descriptive statistics, structural equation model using Statistical Package for the Social Sciences and hypothesis testing and the results are validated.

The results of the study are based on three hypotheses. The findings show that although the financial and the infrastructure factors have positive impact on rate of adoption of EVs in India; the vehicle performance factors have a negative impact on EVs adoption, implying that the respondents of the survey who feel that the vehicle performance factors are the most imperative have a more passive mind-set towards the EVs adoption.

The research work is based on the survey conducted on the pilot region of the national capital region of the country where the majority of the respondents of the survey are conventional fossil fuel vehicles (CFFV) owners. A more accurate analysis on the social factors affecting deployment of EVs in the Indian market can be done if the population of the survey consists of equal share of CFFV and EV owners from all across the nation.

This study will help researchers get a better understanding of the reasons for slow adoption rate of EVs in India. This paper sheds light upon the social factors responsible for the same. The Government of India can use the results of this study to understand the factors responsible for non-adoption and the recommendations for its further work on “Faster Adoption and Manufacturing of (Hybrid) and Electric Vehicles” India scheme.

Results of the study identifies the factors that slow down the adoption rate of EVs in India. The paper suggested potential solutions for the same. Successful implementation in terms of policies and technological advancements can propel India to the top in EV market. Switching to EVs brings about a radical change in the social life of the people and can improve the social status and lifestyle of the people.

Existing research has not focussed much on the social aspects of EVs in India. The present work is solely the result of the strategic thinking, planning, work and implementation by the authors.

As a remedy to usually voluminous, complicated and not easily readable construction contracts, smart contracts can be considered as an effective and alternative solution. However, the construction industry is merely known as a frontrunner for fast adoption of recent technological advancements. Numerous administrative risks challenge construction companies to implement smart contracts. To highlight this issue, this study aims to assess the administrative risks of smart contract adoption in construction projects.

A literature survey is conducted to specify administrative risks of smart contracts followed by a pilot study to ensure that the framework is suitable to the research question. The criteria weights are calculated through the fuzzy analytical hierarchy process method, followed by a sensitivity analysis based on degree of fuzziness, which supports the robustness of the developed hierarchy and stability of the results. Then, a focus group discussion (FGD) is performed to discuss the mitigation strategies for the top-level risks in each risk category.

The final framework consists of 27 sub-criteria, which are categorized under five main criteria, namely, contractual, cultural, managerial, planning and relational. The findings show that (1) regulation change, (2) lack of a driving force, (3) works not accounted in planning, (4) shortcomings of current legal arrangements and (5) lack of dispute resolution mechanism are the top five risks challenging the adoption of smart contracts in construction projects. Risk mitigation strategies based on FGD show that improvements for the semi-automated smart contract drafting are considered more practicable compared to full automation.

The literature is limited in terms of the adoption of smart contracts, while the topic is receiving more attention recently. To support easy prevalence of smart contracts, this study attempts the most challenging aspects of smart contract adoption.

The purpose of this research is to develop a holistic approach to maximize the customer service level while minimizing the logistics cost by using an integrated multiple criteria decision making (MCDM) method for the contemporary transshipment problem. Unlike the prevalent optimization techniques, this paper proposes an integrated approach which considers both quantitative and qualitative factors in order to maximize the benefits of service deliverers and customers under uncertain environments.

This paper proposes a fuzzy‐based integer linear programming model, based on the existing literature and validated with an example case. The model integrates the developed fuzzy modification of the analytic hierarchy process (FAHP), and solves the multi‐criteria transshipment problem.

This paper provides several novel insights about how to transform a company from a cost‐based model to a service‐dominated model by using an integrated MCDM method. It suggests that the contemporary customer‐driven supply chain remains and increases its competitiveness from two aspects: optimizing the cost and providing the best service simultaneously.

This research used one illustrative industry case to exemplify the developed method. Considering the generalization of the research findings and the complexity of the transshipment service network, more cases across multiple industries are necessary to further enhance the validity of the research output.

The paper includes implications for the evaluation and selection of transshipment service suppliers, the construction of optimal transshipment network as well as managing the network.

The major advantages of this generic approach are that both quantitative and qualitative factors under fuzzy environment are considered simultaneously and also the viewpoints of service deliverers and customers are focused. Therefore, it is believed that it is useful and applicable for the transshipment service network design.

This paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.

The critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.

For a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.

Only three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.

The proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.

This research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.