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.

Boarding is one of the major processes of airplane turnaround time, with a direct influence on the airline companies’ costs. From a sustainable point of view, a faster completion of the boarding process has impact not only on the airline company’s long-term performance, but also on customers’ satisfaction and on the airport’s possibility of offering more services without additional investments in new infrastructure. Considering the airplane boarding strategies literature, it can be observed that the latest papers are dealing with developing faster boarding strategies, most of them considering boarding using just one-door of the aircraft. Even though boarding on one-door might be feasible for the airports having the needed infrastructure and sufficient jet-bridges, the situation is different in European airports, as the use of apron buses is fairly common. Moreover, some of the airline companies have adapted their boarding pass in order to reflect which door one should board once they get down from the bus. While using these buses, the boarding strategies developed in the literature are hard to find their applicability. Thus, a new method for boarding on two-door airplanes when apron buses are used is proposed and tested against the actual boarding method. A model is created in NetLogo 6.0.4, taking advantages of the agent-based modeling and used for simulations. The results show a boarding time reduction of 8.91%.

This study explores the dynamic relationship between economic growth, technological innovation, and $C{O}_2$ emissions in BRICS nations, integrating the Environmental Kuznets Curve (EKC) and Innovation Claudia Curve (ICC) frameworks. Using a panel ARDL approach on data from 1991 to 2023, we investigate the long-run and short-run interactions between GDP, renewable energy consumption (RENC), foreign direct investment (FDI), urbanization (URB), and patent applications (PAs) in shaping environmental outcomes. The findings confirm the EKC hypothesis, revealing an N-shaped relationship between GDP and emissions, indicating that while economic growth initially leads to higher $C{O}_2$ emissions, this trend reverses at a critical threshold before a secondary increase occurs at higher income levels. The ICC framework identifies a cubic relationship between innovation and emissions, where technological advancements initially drive higher emissions before contributing to sustainability at later stages, though an excessive scale of innovation may reintroduce environmental pressures. RENC is found to significantly mitigate emissions, while URB and FDI display dual and context-dependent effects, highlighting the multidimensionality of sustainable transitions in emerging economies. These results underscore the importance of targeted policy interventions, such as scaling renewable energy infrastructure, promoting green innovation, guiding urban expansion, and aligning FDI with environmental objectives.