This study provides a bibliometric analysis of the Triple Bottom Line (TBL) framework in sustainable development, highlighting its ethical dimensions and corporate responsibility. Using R Studio and VOSviewer, we examine research trends from 2001 to 2023, revealing a 23.23% annual growth in publications, strong global engagement, and increasing interdisciplinarity. Thematic analysis shows a shift from broad management concepts to specialized areas such as circular economy, life cycle assessment, and climate impact, demonstrating TBL’s transition from a theoretical model to an actionable sustainability framework. Our findings provide practical insights for policymakers and organizations, supporting data-driven decision-making in sustainability strategies. By integrating ethical accountability with economic, social, and environmental goals, TBL offers a structured approach to guiding sustainable business practices and informing policy development.

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field’s impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka’s law is reflected in the author’s contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford’s Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape.

In this study, we explored the dynamic field of fuzzy logic and artificial intelligence (AI) in financial analysis from 1990 to 2023. Utilizing the bibliometrix package in RStudio and data from the Web of Science, we focused on identifying mathematical models and the evolving role of fuzzy information granulation in this domain. The research addresses the urgent need to understand the development and impact of fuzzy logic and AI within the broader scope of evolving technological and analytical methodologies, particularly concentrating on their application in financial and banking contexts. The bibliometric analysis involved an extensive review of the literature published during this period. We examined key metrics such as the annual growth rate, international collaboration, and average citations per document, which highlighted the field’s expansion and collaborative nature. The results revealed a significant annual growth rate of 19.54%, international collaboration of 21.16%, and an average citation per document of 25.52. Major journals such as IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, the Journal of Intelligent & Fuzzy Systems, and Information Sciences emerged as significant contributors, aligning with Bradford’s Law’s Zone 1. Notably, post-2020, IEEE Transactions on Fuzzy Systems showed a substantial increase in publications. A significant finding was the high citation rate of seminal research on fuzzy information granulation, emphasizing its mathematical importance and practical relevance in financial analysis. Keywords like “design”, “model”, “algorithm”, “optimization”, “stabilization”, and terms such as “fuzzy logic controller”, “adaptive fuzzy controller”, and “fuzzy logic approach” were prevalent. The Countries’ Collaboration World Map indicated a strong pattern of global interconnections, suggesting a robust framework of international collaboration. Our study highlights the escalating influence of fuzzy logic and AI in financial analysis, marked by a growth in research outputs and global collaborations. It underscores the crucial role of fuzzy information granulation as a mathematical model and sets the stage for further investigation into how fuzzy logic and AI-driven models are transforming financial and banking analysis practices worldwide.

This paper presents a bibliometric analysis within the research domain dedicated to the utilization of agent-based modeling (ABM) in the field of transportation. By employing specific keywords related to both agent-based modeling and transportation, we have identified and extracted 1016 scholarly papers from the ISI Web of Science database, spanning the period from 2002 to 2023. Through the application of bibliometric methods, we have systematically examined key contributors, affiliations of academic institutions, influential publications, and renowned journals within this domain. Our analysis reveals a consistent and robust growth in scholarly interest pertaining to agent-based modeling in the field of transportation throughout the considered period. Notably, within approximately four decades of ABM’s application in transportation, a distinct upward trajectory began in 2008, culminating in the year 2021. The entire considered period witnessed a remarkable surge in paper production, characterized by an annual growth rate of 21.67%. Furthermore, employing an n-gram analysis, we have delineated and discussed the principal areas within transportation that have progressively benefited from the advancements in agent-based modeling. Prominently, the domains of air transport and road transport have exhibited substantial development over time, while the implications of climate change have emerged as a persistent concern throughout the entire study period.

This study investigates the application of advanced simulation and modeling technologies to optimize the performance and reliability of renewable energy systems. Given the urgent need to combat climate change and reduce greenhouse gas emissions, integrating renewable energy sources into existing infrastructure is essential. Using bibliometric methods, our research spans from 1979 to 2023, identifying key publications, institutions, and trends. The analysis revealed a significant annual growth rate of 16.78% in interest in simulation and modeling, with a notable surge in published articles, reaching 921 in 2023. This indicates heightened research activity and interest. Our findings highlight that optimization, policy frameworks, and energy management are central themes. Leading journals like Energies, Energy, and Applied Energy play significant roles in disseminating research. Key findings also emphasize the importance of international collaboration, with countries like China, the USA, and European nations playing significant roles. The three-field plot analysis demonstrated interconnections between keywords, revealing that terms like “renewable energy sources”, “optimization”, and “simulation” are central to the research discourse. Core funding agencies, such as the National Natural Science Foundation of China (NSFC) and the European Union, heavily support this research. This study underscores the importance of policies and sustainability indicators in promoting renewable energy technologies. These insights emphasize the need for ongoing innovation and interdisciplinary collaboration to achieve a sustainable energy future.

In the context of an emergency, evacuating people from a location in the shortest possible time is essential, as is the high degree of safety that people should expect when evacuating. Lately, in Romania there have been more and more fire events generated by different causes. This article will use agent-based modeling to simulate an emergency evacuation model in NetLogo. The model has been used to perform and analyze various scenarios. With the help of NetLogo, we managed to perform 400 simulations with the evacuation of 180 people (students, teachers, and non-teaching staff) based on which we developed several recommendations to streamline the evacuation process in order to reduce the possibility of death. The present research will help to identify the evacuation times from a school, but it will also highlight certain aspects that may occur during the evacuation. The model that was used in this research took into account the individual particularities of the people taking part in the evacuation, emphasizing the effects that form in a crowd of people when evacuating; effects such as the funnel effect, which is caused by the formation of bottlenecks around narrow areas. All these things are part of the analysis of the measurement of entropy of the exhaust system, a problem that has captured all of the specialists’ attention. Finally, solutions have been proposed to improve evacuation time in case of disasters.