Ethanol obtained from the conversion process of different types of biomass is a renewable source of fuel and since 2010 it has been classified as an “advanced fuel” by the EPA, due to its contribution to the reduction of the impacts of GHG emissions. Recent literature stresses the importance of the use of second-generation fuels to reduce the impacts of the direct and indirect use of land, mostly on agricultural prices. Although these demands constitute a clear clue to R&D activities, there are an impressive number of alternatives, regarding different kinds of biomass, processes and byproducts, a complex matrix of technological opportunities and the demands that generates a clear incentive for collaboration. This paper uses both the Bibliometry and Scientometry approach and the Innovation System (IS) literature under the perspective of Social Networks Analysis (SNA) to build Collaborative Networks (CNs) to the second-generation ethanol (lignocellulosic) using ISI Web of Science database. The adopted procedure emerges once authors, countries and institutions related to bioenergy have incentives to share information in the process of creating a new role in partnership—a network point-of-view. The results show that the United States is in a better position than other countries, improving the role of the university in their IS while China proves to be a great ally of the United States regarding the production of technology to produce lignocellulosic ethanol. Brazil however, does not appear well placed in the network, despite being the second largest producer of first-generation ethanol in the world.
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In more simple terms, the process for obtaining second-generation ethanol consists of "breaking" the lignocellulosic plant material (which may be done physically or through chemical or enzymatic reactions) to obtain the cellulose. In this case sucrose is obtained, and one of the destinations is the production of ethanol. To convert lignocellulosic materials into other products the following steps must be performed: (1) pretreatment of lignocellulosic material in order to increase the exposure of the pulp fibers, facilitating the action of acids or enzymatic hydrolytic agents; (2) use of enzymes from microorganisms such as fungi and bacteria, obtaining sugars by the enzymatic hydrolysis process; and (3) fermentation process of the sugar mixture. See for more details, Brown and Brown (2012), Lee (1997), Sun and Cheng (2002) and Rabelo (2010).
Consists of obtaining ethanol through a fermentation and distillation process from disposable and significant sugars that are in the plants. The main commercial crops are sugarcane, maize, sugar beet, potato and wheat.
Using the same queries for both approaches—bibliometrics and scientometrics—with the content analysis of scientific papers.
The panel sessions were held in a meeting coordinated by BIOEN-Research team during the early 2012 with researchers from bioenergy research centers from the University of São Paulo, State Paulista University (UNESP), State University of Campinas and CTBE.
The VantagePoint version 7. http://www.thevantagepoint.com/.
For this procedure, three different programs were chosen: Microsoft Excel 2013—Data tabulation of The VantagePoint version 7 and exported to UCINET version 6 (import the data, graph building, analysis of indicators for networks and nodes and visualization of relationships among key stakeholders and Gephi version 0.82 beta—This program allows artistic visualization of networks. For the display the Fruchterman-Reingold algorithm was chosen as it best represented the data because of the enormous quantity of relationships. This algorithm represents a force-directed layout because it considers a force between any two nodes. In this algorithm, the nodes are represented by steel rings and the edges have springs between them. The attractive force is analogous to the spring force and the repulsive force is analogous to the electrical force. The basic idea is to minimize the energy of the system by moving the nodes and changing the forces between them (Fruchterman and Reingold 1991). Because of the enormous quantity of data there was a superposition of nodes, even using the algorithm, so the second-step was avoid the superposition of most relevant nodes of the network, setting to the nodes be placed at the bound of the sphere.
KeyWord Plus is a kind of automatic indexing used in the citation databases produced by ISI.
Antonelli, C. (2003). The economics of innovation, new technologies and structural change (studies in global competition). Abingdon: Routledge.
Babcock, B. A., & Pouliot, S. (2013). The economic role of RIN prices CARD policy briefs (Vol. 13, p. 4). Ames: Iowa State University.
Bonaccorsi, A., & Thoma, G. (2007). Institutional complementarity and inventive performance in nano science and technology. Research Policy, 36(6), 813–831. doi:10.1016/j.respol.2007.02.009.
Brown, R. C., & Brown, T. R. (2012). Why are we producing biofuels? Ames, Iowa: Brownia LLC.
Carlsson, B., & Stankiewicz, R. (1991). On the nature, function and composition of technological systems. Journal of Evolutionary Economics, 1(2), 93–118. doi:10.1007/BF01224915.
Croft, W. B. (2000). Combining Approaches to Information Retrieval. In Advances in information retrieval: Recent research from the center for intelligent information (pp. 1–36). Kluwer Academic Publishers.
Dal Poz, M. E. S. (2006). Biotechnology innovation networks: Genomics and intellectual property rights. Campinas: Universidade Estadual de Campinas.
Edquist, C. (2001). The systems of innovation approach and innovation policy: An account of the state of the art. Paper presented at the druid conference, Aalborg.
Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy, 29(2), 109–123. doi:10.1016/S0048-7333(99)00055-4.
Foray, D., & Lundvall, B. A. (1996). The knowledge-based economy: From the economics of knowledge to the learning economy. Paper presented at the unemployment and growth in the knowledge-based economy, Paris.
Freeman, C. (1995). The ‘National System of Innovation’ in historical perspective. Cambridge Journal of Economics, 19(1), 5–24.
Freeman, L. C. (2004). The development of social network analysis: A study in the sociology of science. North Charleston: BookSurge.
Freeman, C., & Soete, L. (2008). A economia da inovação industrial (A. L. S. d. Campos, & J. O. P. d. Costa, Trans., 1 ed., Coleção Clássicos da Inovação). Campinas: Editora da UNICAMP.
Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129–1164. doi:10.1002/spe.4380211102.
Geisler, E. (2000). The metrics of science and technology. Westport: Quorum Books.
Glänzel, W., & Schubert, A. (2005). Analysing scientific networks through co-authorship. In H. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). Netherlands: Springer.
Han, E.-H. S., & Karypis, G. (2000). Centroid-based document classification: Analysis and experimental results. Paper presented at the proceedings of the fourth European conference on the principles of data mining and knowledge discovery (PKDD), Lyon.
HLPE. (2013). Biofuels and food security. A report by the high level panel of experts on food security and nutrition of the committee on World food security. Rome: FAO.
ISI. (2012). Web of science. http://thomsonreuters.com/web-of-science/. Accessed October 24, 2012.
Kajikawa, Y., & Takeda, Y. (2008). Structure of research on biomass and bio-fuels: A citation-based approach. Technological Forecasting and Social Change, 75(9), 1349–1359. doi:10.1016/j.techfore.2008.04.007.
Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18. doi:10.1016/S0048-7333(96)00917-1.
Konur, O. (2011). The scientometric evaluation of the research on the algae and bio-energy. Applied Energy, 88(10), 3532–3540. doi:10.1016/j.apenergy.2010.12.059.
Konur, O. (2012). The scientometric evaluation of the research on the production of bioenergy from biomass. Biomass and Bioenergy, 47, 504–515. doi:10.1016/j.biombioe.2012.09.047.
Larkey, L. S., & Croft, W. B. (1996). Combining classifiers in text categorization. Paper presented at the proceedings of the 19th annual international ACM SIGIR conference on research and development in information retrieval, Zurich, Switzerland.
Laurens, P., Zitt, M., & Bassecoulard, E. (2010). Delineation of the genomics field by hybrid citation-lexical methods: Interaction with experts and validation process. Scientometrics, 82(3), 647–662. doi:10.1007/s11192-010-0177-9.
Leclerc, M., Okubo, Y., Frigoletto, L., & Miquel, J.-F. (1992). Scientific co-operation between Canada and the European Community. Science and Public Policy, 19(1), 15–24. doi:10.1093/spp/19.1.15.
Lee, J. (1997). Biological conversion of lignocellulosic biomass to ethanol. Journal of Biotechnology, 56(1), 1–24. doi:10.1016/S0168-1656(97)00073-4.
Lewis, D. D., & Hayes, P. J. (1994). Guest editorial—special issue on text categorization. ACM Transactions on Information Systems, 12(3), 231.
Leydesdorff, L. (2001). The challenge of scientometrics: The development, measurement and self-organization of scientific communications (2nd ed.). USA: Universal Publishers/uPUBLISH.com.
Liu, W., Gu, M., Hu, G., Li, C., Liao, H., Tang, L., et al. (2014). Profile of developments in biomass-based bioenergy research: A 20-year perspective. Scientometrics, 99(2), 507–521. doi:10.1007/s11192-013-1152-z.
Morone, P., & Taylor, R. (2010). Knowledge diffusion and innovation: Modelling complex entrepreneurial behaviours. Cheltenham: Edward Elgar Publishing Limited.
Mytelka, L., & Farinelli, F. (2000). Local clusters, innovation systems and sustained competitiveness. Paper presented at the meeting on local productive clusters and innovation systems in Brazil: New industrial and technological policies for their development, Rio de Janeiro.
Patel, P., & Pavitt, K. (1994). Uneven (and divergent) technological accumulation among advanced countries: Evidence and a framework of explanation. Industrial and Corporate Change, 3(3), 759–787. doi:10.1093/icc/3.3.759.
Rabelo, S. C. (2010). Evaluation and optimization of pretreatments and enzymatic hydrolysis of the sugarcane bagasse for second generation ethanol production. Campinas: Universidade Estadual de Campinas.
Rausser, G., & Papineau, M. (2008). Managing R&D risk in renewable energy. Paper presented at the transition to a bio economy conferences, risk, infrastructure and industry evolution conference, Berkeley, California.
Saviotti, P. P. (2009). Knowledge networks: Structure and dynamics. In A. Scharnhorst & A. Pyka (Eds.), Innovation networks: New approaches in modelling and analyzing (Understanding Complex Systems) (p. 330). Heidelberg: Springer.
Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28(11), 758–775. doi:10.1016/j.technovation.2008.03.009.
Souza, L. G. A. (2013). Redes de inovação em etanol de segunda geração. Piracicaba: Universidade de São Paulo.
Sun, Y., & Cheng, J. (2002). Hydrolysis of lignocellulosic materials for ethanol production: A review. Bioresource technology, 83(1), 1–11. doi:10.1016/S0960-8524(01)00212-7.
Wagner, C. S., & Leydesdorff, L. (2005). Network structure, self-organization, and the growth of international collaboration in science. Research Policy, 34(10), 1608–1618. doi:10.1016/j.respol.2005.08.002.
Wielen, L. V. D., & Breugel, J. V. (2014). Engineering the BBE: Drop-in or drop-out?. Ravello. Disposable on:http://www.economia.uniroma2.it/icabr-conference/Public/17/file/PPT2013/Auditorium/20-06-2013/5.00-6.00%20Plenary%20IV%20Santaniello%20Lecture/van%20der%20Wielen.pptx.
Willems, P. (2015). Strategic direction in socio-economic issues. Berkeley: Energy Biosciences Institute. Disposable on:http://www-app.igb.illinois.edu/conference/strategicdirections/speakers/paul-willems.ppt.
Zitt, M., & Bassecoulard, E. (2006). Delineating complex scientific fields by an hybrid lexical-citation method: An application to nanosciences. Information Processing and Management, 42(6), 1513–1531. doi:10.1016/j.ipm.2006.03.016.
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de Souza, L.G.A., de Moraes, M.A.F.D., Dal Poz, M.E.S. et al. Collaborative Networks as a measure of the Innovation Systems in second-generation ethanol. Scientometrics 103, 355–372 (2015). https://doi.org/10.1007/s11192-015-1553-2
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