Structure of research on biomass and bio-fuels: A citation-based approach

Biomass and bio-fuels have gained a growing interest as sustainable and renewable energy. In this paper, we perform a citation network analysis of scientific publications to know the current structure of biomass and bio-fuel research. By clustering and visualizing the network, we revealed their taxonomic structure. Emerging technologies are detected by analyzing the average publication year of clusters. According to the results, bio-diesel and hydrogen production are the most rapidly developing domains among biomass bio-fuel researches. We also analyzed the position of each cluster in the global structure of research. By using citation counts within and out of the cluster, we categorized each cluster into the following four categories: (I) topic specific; (II) domain specific; (III) global link; and (IV) specific & global. For research domains of category (III) or (IV), it is difficult that single technology overcomes the current limitation of bio-energy productions. Research on lignocellulose feedstock is a typical case where knowledge from other scientific disciplines is necessary.     

Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications

In this paper, we detect emerging research fronts in a huge number of academic papers related to regenerative medicine, a field of radically innovative research. We divide citation networks into clusters using the topological clustering method, track the positions of papers in each cluster, and visualize citation networks with characteristic terms for each cluster. Analyzing the clustering results with the average published year and parent-child relationship of each cluster could be helpful in detecting recent trends. In addition, tracking topological measures, within-cluster degree z and participation coefficient P, enables us to determine whether there are emerging knowledge clusters. Our results show the success of our method in detecting emerging research fronts in regenerative medicine, and these results are confirmed as reasonable by experts. Finally, we predict the future core papers, with the potential of many citations, via the betweenness centralities in the citation network of the research into adult and somatic stem cells.     

Extracting the commercialization gap between science and technology — Case study of a solar cell

In this paper, we compared structures of the citation network of scientific publications with those of patents, and discussed the differences between them. A case study was performed in a solar cell to develop a method of detecting gaps between science and technology. Scientific research has tended to be more basic, especially in terms of cell design, whereas patents have focused on more applied technology used in solar cell modules. Of the major citation clusters of scientific publications, only two, namely silicon and compound solar cells, corresponded semantically with patent clusters. Conversely, there were no patent clusters corresponding to the other two scientific research fronts, namely dye-sensitized and polymer solar cells. These research areas could be regarded as opportunities for industrial commercialization because scientific activities exist but not technological applications. Our results could offer an intellectual basis for discovering potential opportunities for industrial commercialization.     

Application of technology roadmaps for renewable energy sector

Technology Roadmapping (TRM) is a growing technique widely used for strategy planning and aligning technology with overall business objectives. Technology roadmaps are extensively used in many diverse fields at product, technology, industry, company and national levels. An increasing number of articles published on TRM and technology roadmaps indicate that there is a growing attention for TRM among the researchers from academia, industry and government. In this article, an overview of the application of TRM in renewable energy sector has been provided. After survey of the relevant academic literature and industry roadmaps, we tried to group the roadmaps related to the renewable energy technologies into national, industry/sector and organizational level roadmaps. Research findings indicate that goals and objectives of renewable energy roadmaps are different at these three levels. At national level, roadmaps focus on future energy security, energy dependence, energy policy formulation and environment protection. At industry/sector level, roadmaps are used to identify vision, common needs and evaluate barriers, constraints and risks faced by the industry from technical, political and commercial aspects. Organizational roadmap focuses on evaluation and prioritization of R&D projects to achieve the business goals. Similarly different methods, tools and approaches are used to develop roadmaps at different levels. Various other characteristics of these roadmaps are also discussed and analyzed. Research findings also indicate that greater numbers of roadmaps are developed for those renewable energy technologies undergoing rapid growth. Moreover, most of these roadmaps are developed in the regions where more research, development and deployment activities of renewable energy technologies is taking place.     

Knowledge dynamics and sources of eco-innovation: Mapping the Green Chemistry community

Over the last few decades, the interest for developing a more sustainable chemistry has increased worldwide and has triggered the proliferation of new knowledge. The present article aims at investigating the dynamics of scientific knowledge underlying this emergent field, the main countries and organizations involved, and the factors that have shaped the evolution of the field. In order to circumscribe such a still fluid area of research, we first show how an epistemic community around the concept of Green Chemistry (GC) has emerged and materialized. We then build an original dataset of scientific publications generated by this community and apply two algorithms for the analysis of citation networks. That allows us to identify and analyze the scientific knowledge that laid the foundations of the GC community and the main scientific trajectory that emerged along its whole evolution. The results highlight that the GC community, strongly supported by the US EPA, has grown exponentially since 2000 and has spread among a wide range of countries, including emerging countries. The results also suggest that policy and industry interests, as well as regulation, have played a significant role in shaping the emergence and evolution of GC.     

A review of machine learning for big data analytics: bibliometric approach

ABSTRACT

The amalgamation of machine learning and big data has led to a revolution in data science with several influencing applications to various domains. To gain insights on the current research trends on machine learning for big data analytics, this study follows a bibliometric analysis methodology of citation data to review and quantitatively assess the explosion and impact of literature and research performance in this vibrant research area, which has witnessed rapid changes and rising interest in business, industry and academia. Using a variety of bibliometric measures and visualisation techniques, the paper examines and identifies several related issues including research productivity and directions, major contributors, publication trends and growth rates, citation and collaboration analysis, and others. The relevant bibliographic units for the study were collected from the Core Collection of the Web of Science bibliographic database. Nearly all the relevant publications prior to February 2018 were included in the analysis. The overwhelming productivity and wide-spread applications in several multidisciplinary domains have been revealed, with one-to-two ratio of journal to conference publications. Three countries (USA, China, India) are dominating the research output with more than two-thirds of the total productivity.     

Citation network analysis of organic LEDs

The field of organic light-emitting diodes (OLEDs) is an emergent research domain because of both scientific interest in chemistry and condensed matter physics and industrial importance as flat panel displays. In this paper, we analyzed a citation network of OLED papers and used a topological clustering method to investigate the structure of research and to detect emerging research domains. We found that most papers belong to two main clusters: organics and polymers. These two clusters have distinctive differences in subcluster structures and journals where papers are published. Supposing this discrepancy to indicate research progress, organics are in the applied research stage, while polymers are in the basic research stage.     

Contributions to financial crisis research: an assessment of the literature in Social Science Citation Index journals from 1990 to 2008

Research in the field of financial crisis has generated a considerable amount of literature in the past, yet there has neither been any study to assess the contributions to the literature made by individuals or institutions, nor any to measure the impact of the articles and researchers. This study represents an attempt to: (1) investigate the major journals in publishing financial crisis research, (2) assess the contributions of individual researchers and institutions using the credited contribution approach and (3) measure the impact of individual publications and individual researchers on the financial crisis literature through citation analysis, based on the publications in Social Science Citation Index (SSCI) journals from 1990 to 2008. The findings provide a useful benchmark for assessing individual and institutional research productivity, and trends for future research and venues for publications are identified.     

Regional and copula estimation effects on EU and US energy equity portfolios

ABSTRACT

The aim of this paper is to investigate the regional interdependence structure of energy equities in the US and in the EU. Based on weekly stock prices of 28 big energy firms in the two regions from 2008 to 2019, we compare the efficiency of using bivariate or multivariate copulas to describe the dependence structure of energy equities. Furthermore, we investigate the impact of the choice between these two methods on the performance of energy equity portfolios. Our empirical results show that multivariate copulas, such as C-Vine, allow to better describe the dependence structure of energy equities. We also find that there is a stronger and more complex dependence structure among EU energy equities than among US energy equities. Our scenario analysis also shows that the dependence structure is stronger during the GFC while being weaker during the ESDC. More importantly, the correlation matrix obtained from the multivariate copula method allows to obtain optimal mean-CVaR portfolios with a higher performance than that from the bivariate copula method. More importantly, optimal portfolios constituted with multivariate copulas allow to reduce the portfolio’s sensitivity to oil prices.     

中国绿色能源高质量发展水平测度研究

在"创新、协调、绿色、开放、共享"五大发展理念中,绿色协调发展成为关系中国可持续发展全局的重要理念,而绿色能源高质量发展是绿色高质量发展中的一个重要有机组成部分。本文构建了涉及绿色能源投资、生产、消费和减排4个系统的9个细化指标,利用熵权TOPSIS法实证测度2017年中国绿色能源高质量发展水平,并深入分析各区域之间的差异。通过研究发现:中国30个省区绿色能源高质量发展的水平在4个子系统和综合水平方面的表现均存在差异,综合水平总体呈现"东中部高且比较接近,而西部中等偏低"的区域分布格局;然后依照绿色能源高质量发展综合水平的高低,把30个省区划分为领先型、中等型和落后型3种类型。进而深入把握中国绿色能源高质量发展水平的区域分布规律,为统筹推进各省区协同提升中国绿色能源高质量发展水平提供较为可靠的依据。