ABSTRACT
The crude oil-based gasoline fuels have been widely used in the transportation sector since the 1920s. However, there have been great public concerns over the adverse environmental and human impact of these fuels. Hence, biomass-based bioethanol fuels have increasingly been used in blending gasoline fuels, in the fuel cells, and in the biochemical production in a biorefinery context. Bioethanol fuels also play a critical role in maintaining the energy security in the supply shocks related to oil price shocks, COVID-19 pandemics, or wars in the aftermath of the Russian invasion of Ukraine. However, it is necessary to pretreat the biomass to enhance the yield of the bioethanol prior to the bioethanol production through the hydrolysis of the biomass and fermentation of the resulting hydrolysates. One of the most-studied feedstocks for the bioethanol fuels has been the lignocellulosic biomass at large. The research in the field of the lignocellulosic biomass-based bioethanol fuels has intensified in this context in the key research fronts of the pretreatment of the lignocellulosic biomass and to a lesser extent hydrolysis of the lignocellulosic biomass, fermentation of the lignocellulosic biomass-based hydrolysates, and production and evaluation of the lignocellulosic biomass-based bioethanol fuels. Thus, it emerges as a distinctive research field, complementing the research on the second generation bioethanol fuels from the agricultural residues as well as food, industrial, urban, and forestry wastes among others. However, it is essential to develop efficient incentive structures for the primary stakeholders to enhance the research in this field. The scientometric analysis has been used in this context to inform the primary stakeholders about the current state of the research in a selected research field. As there have been no published scientometric studies in this field, this book chapter presents a scientometric study of the research in the lignocellulosic biomass-based bioethanol fuels. It examines the scientometric characteristics of both the sample and population data presenting scientometric characteristics of these both datasets in the order of documents, authors, publication years, institutions, funding bodies, source titles, countries, Scopus subject categories, Scopus keywords, and research fronts.
