Scientific and Technological activities in Biofuel R&D: Are they moving hand in hand? Institute of Economics, University of Campinas, Brazil. Carolina da Silveira Bueno, José Maria F.J. da Silveira, Antonio Marcio Buainain. nea@eco.unicamp.br University of California, Berkeley, XXI ICABR CONFERENCE May 30- June 2, 2017.

A general view of the problem An interesting fact is that the R&D is based on scientific collaboration networks by institutions worldwide. Recent works based on scientometrics (Souza et al. 2015; Kajikawa, Y., & Takeda, 2008) dealing with biofuels confirm this clear trend. The continuous generation of a variety and modularity of technology by a large number of research organizations in many countries suggests that bionergy, specifically biofuels, are an emergent, yet still immature, sector (Winskel et al, 2014). However, some pattern exist.: see the next slide=> country led, biomass led research

SUGARCANE – INSTITUITIONS

CORN – INSTITUITIONS

Methodology: scientific papers Data bank from WOS and from Dewent Patent databank. 1st Step: Problem, an the broad coverage and a variety of query combinations are used between the years 1970 and 2015 to meet biofuels in “SUGARCANE” and "Corn" (1ª data sample) and then the articles (2ª data sample) are treated to meet the research domain. At this stage, the software VantagePoint eliminates those that are not relevant to the research. 2st Step At this stage, the software VantagePoint analyses the connections and generates the international collaboration cluster 3s is analyzed the metrics the sub-networks. At this stage, the software VantagePoint is used to analysis the metrics and visualization of networks, in that the countries and research areas will be discussed.

Methodology: patents In order to obtain the patents from the Derwent database, the tool IPC STATS Search was used to classify patents and utility models, according to the different technology-areas to which they belong. Sample construction made use of the Derwent Innovations Index, owned by Thomson Reuters. The combination of terms from IPC STATS was as follows: or biofuels* or biomass or bioenergy)) ((C12P-007/06 OR C12P-007/08 OR C12P-007/10 OR C12P-007/14 C12P-019/14 OR C12P-039/00 OR C12N-001/15 OR C12N-001/16 OR C12N-001-18 OR C12N-001/19 OR C12N-001/20 OR C12N-001/21 OR C12N-001/22 OR C12N-009/02 OR C12N-009/04 OR C12N-009/14 OR C12N-015/01 OR C12N-015/02 OR C12N-015/03 OR C12N-015/04 OR C12N-015/05 OR C12N-015/10 OR C12N-015/11) AND (SUGARCANE or CORN* AND (ethanol*

Block modelling The methodology is based on Bueno et al. (2016) which provides an analytical treatment to the matrix of co-occurrence of scientific collaboration of countries/organizations using the software Pajek; Two procedures were carried out: a) block modeling; b) comparisons between general and sub-networks.

Methodology Clusters were generated by the method, from the software, by an autocorrelation matrix. This matrix shows the co-authorship by areas of knowledge. In the case of block models, the same. It shows what are the areas of the papers are and how they are correlated, thus forming clusters by areas of knowledge. In the case of the IPC network, this is a co-occurrence matrix. It clusters by approximation, forming the knowledge blocks, for example, biomass, 2 and 3 generation ethanol. In this case, the classes of IPCs originate from the ethanol cluster, that is, these clusters directed the formation of other clusters, such as cellulose and biomass

COLLABORATIVE NETWORKS- 1975-2014 Sugarcane Degree Brazil 32.000 USA 40.000 India 19.000 Australia 28.000 China 16.000 South Africa Japan 15.000 Germany 25.000 Pakistan 12.000 Corn Degree USA 68.000 China 30.000 Canada 34.000 Brazil 26.000 Germany 46.000 Japan 27.000 India 19.000 Spain 32.000 France

Collaborative network in áreas-1975-2014 Research Areas Articles Collaboration % Agriculture 4.760 2.675 56,20 Plant Sciences 2.290 1.360 59,39 Biotechnology 1.830 1.488 81,31 Energy fuels 732 312 42,62 Biochemistry/Molecular biology 603 352 58,38 Chemistry 595 271 45,55 Environmental Science 392 79,59 Engineering 331 280 84,59 Genetics 304 292 96,05 Microbiology 283 175 61,83

COLLABORATIVE NETWORK – ARTICLES 1970/1990 CORN SUGARCANE 10 COUNTRIES 17 COUNTRIES

AUTO-CORRELATION MAP IN RESEACH AREA 1970/1990 AUTO-CORRELATION MAP IN RESEACH AREA In this initial phase (1970 to 1990) of analyzing the areas of research and the countries on the network, the block models are presented on the network as follows: block modeling A – United States block modeling B – Brazil, India and Australia Figure. Collaborative network among research areas - sugarcane ethanol and corn ethanol. 1970-1990. Source: Authors’ research results.

COLLABORATIVE NETWORK – ARTICLES Countries- corn ethanol and sugarcane ethanol 1991/1999

COLLABORATIVE NETWORK – ARTICLES 2000/2014

Figure. Collaborative network among research areas - sugarcane and corn ethanol. 1991-2014. Source: Authors’ research results.

clusters C12P7/08 C12P7/10 Cluster C12P706 A B C A B C Fig. Visualization of the collaborative network in IPCs. 16

PATENTS: CORRELATION MAP IN BIG RESEARCH AREAS Graf. Patents. Year.

Network for an interactive scheme oriented by a minimal network extension tree, in order to generate coordinates for the nodes expressing inter-cluster and intra-cluster connections . Enchainement An advantage of the method is to point to the path of the extreme values , as additional clusters

Timeline Block modeling patents

BLOCK MODELING IN IPCs PATENTS biomass Ethanol A Cellulosic C Biomass wood 20

Conclusions The block models of knowledge analyzed over different time periods showed that the paradigm is in development. A more robust appearance of the networks in terms of collaboration structure and production of articles, together with the insertion of hundreds of countries, took place after the 2000s, in which 4 decades were necessary to reach the current stage of development. ii) The technological areas of the patents are interdependent, as can also be observed in the knowledge networks of the articles. This is an extremely important result, given that fundamentally, in this case, it leads to technological variety. Interdependence between the areas generated technological variety for third generation ethanol (from algae) on the sugarcane network, in the same way in which biotechnology generated technological variety for second generation ethanol.

iii) This more robust structure of co-authorship of articles and interdependence between areas of knowledge could indicate a pattern related to the paradigm’s development, in which international research collaboration networks appear as a structure to regulate the development of these products of knowledge. Conclusions