The paradigm shift towards sustainable transportation underscores the burgeoning focus on electric vehicles (EVs) as a viable alternative to combustion-powered counterparts. Concurrently, the corpus of scholarly publications exploring this domain has expanded, endeavoring to address multifaceted challenges across various disciplines. Among the methodologies enlisted, Multi-criteria Decision Analysis (MCDM) emerges as a pivotal tool for decision-makers, facilitating the resolution of complex problems characterized by multiple criteria and alternatives. This research employs a modified Systematic Literature Review (SLR) methodology, integrating the Analytical Hierarchical Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for article ranking. This novel approach not only enhances the precision of the ranking process but also earmarks the articles exerting substantial influence on the scholarly landscape. The exhaustive review culminates in a curated portfolio of 73 seminal articles, with a pronounced emphasis on Charging Stations applications, accounting for approximately 42.46% of the collective focus. This study's findings illuminate the prevailing trends within the nexus of EV research and MCDM, delineating a trajectory for future inquiries and applications. In doing so, it underscores the indispensable role of MCDM in navigating the complexities inherent in the transition to electrified mobility solutions. The meticulous application of the SLR methodology, augmented by AHP and TOPSIS, not only refines the academic discourse but also paves the way for a more structured and impactful exploration of EVs within the realm of sustainable transportation.

Oil and gas refineries play a key role in the economies of countries by providing energy to various industrial sectors. A lack of an integrated efficiency analysis procedure, in many industries, could significantly impact the planning of sustainable industrial structures and operations. It also can influence company performance and competitiveness, and, eventually, negatively compromise the fuel supply process. All these problems taken together might negatively impact the environment and sustainable practices. Studies of efficiency in the oil industry can help to reduce its environmental and social impact and to achieve long-term green transition goals. In this work, the data envelopment analysis (DEA) method was used to present improvement goals for production units, based on efficiency indexes. Furthermore, the DEA window analysis model, integrated with the Malmquist index and cluster analysis, was used to evaluate efficiency and the factors that explain the differences between refineries in a number of timeframes. A numerical analysis was carried out with data collected from 12 Brazilian oil refineries between 2012 and 2020, using DEA window analysis, cluster analysis, and the Malmquist index.

This study investigates the application of Multi-Criteria Decision Analysis (MCDA) methods to the classification of research papers within a Systematic Literature Review (SLR). Distinctions are drawn between compensatory and non-compensatory MCDA approaches, which, despite their distinctiveness, have often been applied interchangeably, leading to a need for clarification in their usage. To address this, the methods of Entropy Weight Method (EWM), Analytic Hierarchy Process (AHP), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were utilized to determine the parameters for ranking papers within an SLR portfolio. The source of this ranking comprised publications from three major databases: Scopus, ScienceDirect, and Web of Science. From an initial yield of 267 articles, a final portfolio of 90 articles was established, highlighting not only the compensatory and non-compensatory classifications but also identifying methods that incorporate features of both. This nuanced categorization reveals the complexity and necessity of selecting an appropriate MCDA method based on the dataset characteristics, which may exhibit attributes of both approaches. The analysis further illuminated the geographical distribution of publications, leading contributors, thematic areas, and the prevalence of specific MCDA methods. This study underscores the importance of methodological precision in the application of MCDA to systematic reviews, providing a refined framework for evaluating academic literature.