• Energy Research

The research highlights the importance of sustainable supply chain management (SSCM), technology adoption (TA), and performance measurement in promoting sustainability and improving supply chain performance. By incorporating sustainable practices and utilizing digital technologies, organizations can create a more sustainable future and improve their overall performances. This study conducted an in-depth review of the literature to investigate the presence of TA in SSCM with a focus on digital-based supply chains. The review used both bibliometric and content analysis methods to analyze relevant research articles, with the goal of providing a comprehensive understanding of the current state of research in the field, identifying any gaps in the literature, and providing direction for future research. The content analysis of the literature showed the absence of concrete frameworks for SSCM and the need for clearer and more applicable sustainability measurement indices. To address this gap, the study proposed a framework for achieving sustainable development goals through SSCM. In addition, a framework for deploying sustainability indicators was presented. The proposed framework can be used by practitioners to develop practical and comprehensive measures for their respective industries.

The main purpose of this paper is to present a systematic review of MCDM techniques and approaches in sustainable and renewable energy systems problems. This study reviewed a total of 54 papers published from 2003–2015 in more than 20 high-ranking journals, most related to sustainable and renewable energies, and which were extracted from the Web of Science database. In the category of application areas, papers were classified into two main groups: (1) sustainable energy and (2) renewable energy. Furthermore, in the classification of techniques and approaches, the papers were categorized into six groups: (1) AHP and F-AHP; (2) ANP and VIKOR; (3) TOPSIS and F-TOPSIS; (4) PROMETHEE; (5) integrated methods and (6) other methods. In addition, papers were reviewed based on the authors’ nationalities, the publication date, techniques and approaches, the name of journal and studies criteria. The results of this study indicated that, in 2015, scholars have published more papers than in other years. Furthermore, AHP/fuzzy AHP and integrated methods were ranked as the first rank, with 14 papers. Additionally, Journal of Renewable Energy is the first journal, with 16 publications, and this was the most significant journal in this study. Findings of this review paper confirm that MCDM techniques can assist stakeholders and decision makers in unravelling some of the uncertainties inherent in environmental decision making, and these techniques demonstrate a growing interest of previous scholars to apply these techniques for solving different stages of sustainable and renewable energy systems.

<abstract> <p>Power-efficient wireless sensor network routing techniques (WSN). Clustering is used to extend WSNs' lifetimes. One node act as the cluster head (CH) to represent the others in communications. The member nodes are less important than the cluster hub (CH) in the clustering procedure. Fuzzy techniques based on clustering theory may provide evenly distributed loads. In this study, we provide a fuzzy-logic-based solution that factors in distance to base station (BS), number of nodes, remaining energy, compactness, distance to communicate within a cluster, number of CH, and remaining energy. Fuzzy clustering has a preliminary and final step. First, we select CH based on distance to the base station (BS), remaining node vigor, and node compactness. In the second phase, clusters are created by combining nodes that aren't already in a CH, using density, outstanding vigor, and detachment as limitations. The proposed solution increases load balancing and node longevity. This work provides a unique hybrid routing technique for forming clusters and managing data transfer to the base station. Simulation findings confirm the protocol's functionality and competence. Reduced energy use keeps network sensor nodes online longer. The framework outperforms Stable Election Protocol (SEP), hybrid energy-efficient distributed clustering (HEED), and Low Energy Adaptive Clustering Hierarchy (LEACH). Using the nodes' energy levels to create a grid pattern for the clusters gave four clusters. In addition, the proposed method has a 4347%, 41.46%, 39.26%, 37.57% and 35.67% reduction in average energy consumption when compared with the conventional algorithms. The proposed technologies could increase the network's lifetime, stability interval, packet transfer rate (throughput), and average energy. The suggested protocol is at least 50% better in every statistic that was looked at, such as network lifetime, stability interval, packet transmission rate (throughput), and average energy use.</p> </abstract>

Most methodological areas assume common serious reflections to certify difficult study and publication practices, and, therefore, approval in their area. Interestingly, relatively little attention has been paid to reviewing the application of Structural Equation Modeling (SEM) in environmental sustainability problems despite the growing number of publications in the past two decades. Therefore, the main objective of this study is to fill this gap by conducting a wide search in two main databases including Web of Science and Scopus to identify the studies which used SEM techniques in the period from 2005 to 2016. A critical analysis of these articles addresses some important key issues. On the basis of our results, we present comprehensive guidelines to help researchers avoid general pitfalls in using SEM. The results of this review are important and will help researchers to better develop research models based on SEM in the area of environmental sustainability.

Bioenergy is a kind of renewable energy that can potentially contribute to a broad spectrum of economic, environmental, and societal objectives and aid sustainable development. The assessment, management, and monitoring of the diverse bioenergy production technology alternatives are complex in nature and deliver different benefits due to the lack of precise and comprehensive data. Selection of an optimal bioenergy production technology (BPT) alternative is considered a complex multi-criteria decision-making (MCDM) problem that involves many incompatible tangible and intangible as well as qualitative and quantitative criteria. The procedure of defining and evaluating the weights of the criteria is an important concern for decision experts because the assessment and the final selection of the BPT alternative are carried out on the basis of the defined set of criteria. Intuitionistic fuzzy sets (IFSs) have received considerable attention due to their ability to handle the imprecision and vagueness that can arise in real-life situations. Thus, this study presents an integrated approach, based on stepwise weight assessment ratio analysis (SWARA) and complex proportional assessment (COPRAS) approaches, for the selection of BPT alternatives. In the integrated framework, criteria weights are determined by the SWARA procedure, and the ranking of BPT alternatives is decided by the COPRAS method using IFSs. The criteria weights evaluated by this approach involve the imprecision of experts’ opinions, which makes them more comprehensible. To express the efficiency and applicability of the integrated framework, a BPT selection problem is presented using IFSs. In addition, this study involved sensitivity analysis with respect to various sets of criteria weights to reveal the strength of the developed approach. The sensitivity analysis outcomes indicate that the agricultural and municipal waste of biogas (S3) consistently secures the highest rank, despite how the criteria weights vary. Finally, a comparative study is discussed to analyze the validity of the obtained result. The findings of this study confirm that the proposed framework is more useful than and consistent with previously developed methods using the IFSs environment.

Global economic trends have shown the progression of social inequalities and environmental deterioration in the grey economy. New economic practices and policies need to be developed in order to achieve the sustainable development goals (SDGs). A green economy (GE) has a correlative role with the implementation of sustainable development (SD), which could revive the grey economy, human well-being, and social equity, as well as substantially decrease environmental risks and ecological scarcities. This study aims to develop a hybrid methodological and mathematical approach to prioritize the most effective variables from classified GE and SDGs criteria (23 criteria) to implement SD. This study has deliberated over the Decision making trial and evaluation laboratory (DEMATEL) technique for considering interconnections among numerous criteria to collect the most effective variables (12 criteria) based on three pillars (3Ps) of SD. Likewise, the analytic network process (ANP) technique ranked these effective variables by considering their network relations based on three indicators. Lastly, integration was used to finalize and prioritize the most effective variables based on their weight from the ANP technique. This study will highlight the green economy with exclusive environmental issues and sustainable growth as the greatest effective variables among GE and SDGs criteria for SD implementation.

As an attractive generalization of the intuitionistic fuzzy set (IFS), q-rung orthopair fuzzy set (q-ROFS) provides the decision makers (DMs) with a wide window for preference elicitation. Previous studies on q-ROFS indicate that there is an urge for a decision framework which can make use of the available information in a proper manner for making rational decisions. Motivated by the superiority of q-ROFS, in this paper, a new decision framework is proposed, which provides scientific methods for multi-attribute group decision-making (MAGDM). Initially, a programming model is developed for calculating weights of attributes with the help of partially known information. Later, another programming model is developed for determining the weights of DMs with the help of partially known information. Preferences from different DMs are aggregated rationally by using the weights of DMs and extending generalized Maclaurin symmetric mean (GMSM) operator to q-ROFS, which can properly capture the interrelationship among attributes. Further, complex proportional assessment (COPRAS) method is extended to q-ROFS for prioritization of objects by using attributes’ weight vector and aggregated preference matrix. The applicability of the proposed framework is demonstrated by using a renewable energy source prioritization problem from an Indian perspective. Finally, the superiorities and weaknesses of the framework are discussed in comparison with state-of-the-art methods.

Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement in production and management. Earlier studies on IoTs show evidence that direct adoption of such digital technology is an ordeal and incurs adoption barriers that must be prioritized for effective management. Motivated by the claim, in this paper, the authors attempt to prioritize the diverse adoption barriers with the support of the newly proposed Fermatean fuzzy-based decision framework. Initially, qualitative rating information is collected via questionnaires on barriers and criteria from the circular economy (CE). Later, these are converted to Fermatean fuzzy numbers used by integrated approaches for decision processes. A regret scheme is put forward for determining CE criteria importance, and the barriers are prioritized by using a novel ranking algorithm that incorporates the WASPAS formulation and experts’ personal choices during rank estimation. The applicability of the developed framework is testified via a case example. Sensitivity analysis and comparison reveal the merits and limitations of the developed decision model. Results show that labor/workforce skill insufficiency, an ineffective framework for performance, a technology divide, insufficient legislation and control, and lack of time for training and skill practice are the top five barriers that hinder IoT adoption, based on the rating data. Additionally, the criteria such as cost cutting via a reuse scheme, resource circularity, emission control, and scaling profit with green habits are the top four criteria for their relative importance values. From these inferences, the respective authorities in the clean energy sector could effectively plan their strategies for addressing these barriers to promote IoT adoption in the clean energy sector.