Sustainability, Big Data and Mathematical Techniques: A Bibliometric Review

Abstract

This article has reviewed international research, up to the first half of 2021, focused on sustainability, big data and the mathematical techniques used for its analysis. In addition, a study of the spatial component (city, region, nation and beyond) of the works has been carried out and an analysis has been made of which Sustainable Development Goals (SDGs) have received the most attention. A bibliometric analysis and a fractal cluster analysis were performed on the papers published in the Web of Science. The results show a continuous increase in the number of published articles and citations over the whole period, demonstrating a growing interest in this topic. China, the United States and India are the most productive countries and there are more papers at the regional level. It has been found that the environmental dimension is the most studied and the least studied is the social dimension. The mathematical techniques used in the empirical work are mainly regression analysis, neural networks and multi-criteria decision methods. SDG9 and SDG11 are the most worked on. The trend shows a convergence in recent years towards big data applied to supply chains, Industry 4.0 and the achievement of sustainable cities.

A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences

“…In addition to these overall research, focused studies on bibliometric analysis o academic literature on Big Data and/or AI ranges from explainable artificial intellig [42], engineering applications [43], group decision-making [44]; business intelligence analytics [45], sustainability [46]; circular economy [47], supply chain management [4 Research on the bibliometrics of Big Data and AI publications either focus on earlier periods, such as Niu et al [34] (2016)'s work on global research on artificial intelligence from 1990-2014 and Kalantari et al [35] (2017)'s study on trends in Big Data research for 1980-2015, or they use limited number of keywords such as in Raban & Gordon [36] (2020) who used only "Big Data" or "Mega Data" when collecting their data sets in the format of published articles that are categorized with these keywords. Other studies focus points include but are not limited to author cooperations [37], interdisciplinarity [38], visualisation [39] and international collaborations [40].…”

“…In addition to these overall research, focused studies on bibliometric analysis of the academic literature on Big Data and/or AI ranges from explainable artificial intelligence [42], engineering applications [43], group decision-making [44]; business intelligence and analytics [45], sustainability [46]; circular economy [47], supply chain management [48] to higher education [49,50]. Despite this recent but rich literature, Big Data and AI applications aspect has not been investigated from the social sciences perspective.…”

A Fuzzy-Set Qualitative Comparative Analysis of Publications on the Fuzzy Sets Theory

“…The bibliometric analysis used the VOS Viewer 1.6.17 software [27] to study the networks of keywords, countries and journals. VOS Viewer has been used in multiple previous studies and is a tool with high precision of results [28][29][30]. The evaluation of specific articles on fuzzy set, fuzzy logic, mathematics and sustainability was carried out with a systematic review that allowed identifying publication opportunities on new emerging topics.…”