Incorporating Green Bonds into Portfolio Investments: Recent Trends and Further Research

Marín-Rodríguez, Nini Johana; González-Ruiz, Juan David; Valencia-Arias, Alejandro

doi:10.3390/su152014897

Cited by 13 publications

(9 citation statements)

References 105 publications

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“…As suggested by Marin-Rodriguez et al [34], the analysis is divided into two main stages: the dataset extraction and the bibliometric analysis, as depicted in the following.…”

Section: Methodsmentioning

confidence: 99%

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

Sandu,

Ioanăș,

Delcea

et al. 2024

Algorithms

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Fake news is an explosive subject, being undoubtedly among the most controversial and difficult challenges facing society in the present-day environment of technology and information, which greatly affects the individuals who are vulnerable and easily influenced, shaping their decisions, actions, and even beliefs. In the course of discussing the gravity and dissemination of the fake news phenomenon, this article aims to clarify the distinctions between fake news, misinformation, and disinformation, along with conducting a thorough analysis of the most widely read academic papers that have tackled the topic of fake news research using various machine learning techniques. Utilizing specific keywords for dataset extraction from Clarivate Analytics’ Web of Science Core Collection, the bibliometric analysis spans six years, offering valuable insights aimed at identifying key trends, methodologies, and notable strategies within this multidisciplinary field. The analysis encompasses the examination of prolific authors, prominent journals, collaborative efforts, prior publications, covered subjects, keywords, bigrams, trigrams, theme maps, co-occurrence networks, and various other relevant topics. One noteworthy aspect related to the extracted dataset is the remarkable growth rate observed in association with the analyzed subject, indicating an impressive increase of 179.31%. The growth rate value, coupled with the relatively short timeframe, further emphasizes the research community’s keen interest in this subject. In light of these findings, the paper draws attention to key contributions and gaps in the existing literature, providing researchers and decision-makers innovative viewpoints and perspectives on the ongoing battle against the spread of fake news in the age of information.

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“…As suggested by Marin-Rodriguez et al [34], the analysis is divided into two main stages: the dataset extraction and the bibliometric analysis, as depicted in the following.…”

Section: Methodsmentioning

confidence: 99%

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

Sandu,

Ioanăș,

Delcea

et al. 2024

Algorithms

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“…Figure 1 acts as the initial foundation for the indicated discussion, highlighting two primary phases, dataset extraction and bibliometric analysis. This methodological approach is similar to the one suggested in the scientific literature for conducting a bibliometric analysis [20,21]. The first part refers to the stages followed by the authors in order to generate the relevant dataset for the present analysis.…”

Section: Methodsmentioning

confidence: 99%

Guiding Urban Decision-Making: A Study on Recommender Systems in Smart Cities

Sandu,

Cotfas,

Stănescu

et al. 2024

Electronics

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In recent years, the research community has increasingly embraced topics related to smart cities, recognizing their potential to enhance residents’ quality of life and create sustainable, efficient urban environments through the integration of diverse systems and services. Concurrently, recommender systems have demonstrated continued improvement in accuracy, delivering more precise recommendations for items or content and aiding users in decision-making processes. This paper explores the utilization of recommender systems in the context of smart cities by analyzing a dataset comprised of papers indexed in the ISI Web of Science database. Through bibliometric analysis, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors were extracted. The findings revealed an average annual scientific production growth of 25.85%. Additionally, an n-gram analysis across keywords, abstracts, titles, and keywords plus, along with a review of selected papers, enriched the analysis. The insights gained from these efforts offer valuable perspectives, contribute to identifying pertinent issues, and provide guidance on trends in this evolving field. The importance of recommender systems in the context of smart cities lies in their ability to enhance urban living by providing personalized and efficient recommendations, optimizing resource utilization, improving decision-making processes, and ultimately contributing to a more sustainable and intelligent urban environment.

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“…According to Marin-Rodriguez et al [41], two main steps are needed for conducting a bibliometric analysis: the identification of the dataset and the analysis of the extracted dataset. As a result, the steps presented in Figure 1 have been considered.…”

Section: Methodsmentioning

confidence: 99%

From Data to Insights: A Bibliometric Assessment of Agent-Based Modeling Applications in Transportation

Domenteanu,

Delcea,

Chiriță

et al. 2023

Applied Sciences

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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.

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Incorporating Green Bonds into Portfolio Investments: Recent Trends and Further Research

Cited by 13 publications

References 105 publications

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

Numbers Do Not Lie: A Bibliometric Examination of Machine Learning Techniques in Fake News Research

Guiding Urban Decision-Making: A Study on Recommender Systems in Smart Cities

From Data to Insights: A Bibliometric Assessment of Agent-Based Modeling Applications in Transportation

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