1. 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.
University of California, Berkeley, XXI ICABR CONFERENCE
May 30- June 2, 2017.

2. 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, ) 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, ).
However, some pattern exist.: see the next slide=> country led, biomass led research

3. SUGARCANE – INSTITUITIONS

4. CORN – INSTITUITIONS

5. 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.

6. 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 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*

7. 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.

8. 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

9. 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

10. 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

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

12. 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
Source: Authors’ research results.

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

14. COLLABORATIVE NETWORK – ARTICLES 2000/2014

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

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

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

18. 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

19. Timeline
Block modeling patents

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

21. 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.

22. 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