TY - GEN
T1 - The spatial distribution of knowledge production in Europe. Evidence from KET and SGC
AU - Lepori, Benedetto
AU - Guerini, Massimiliano
AU - Scherngell, Thomas
AU - Laredo, Philippe
PY - 2019/8/1
Y1 - 2019/8/1
N2 - In this paper, we develop an analysis of the spatial distribution of knowledge production related to Key Emerging Technologies (KETs) and Societal Grand Challenges (SGCs) in Europe building on an extensive dataset developed in the H2020 KNOWMAK project. We first provide a broad characterization of European regions in terms of their knowledge volume and knowledge intensity, which leads to a distinction between the large metropolitan regions and smaller knowledge intensive regions. Second, by using principal component analysis, we identify two components of knowledge production that we broadly characterize as academic production and technology production. This distinction allows further categorizing regions in terms of the balance between the two components, which we suggest is also related to the ecology of actors in a region and, notably, of the importance of public-sector research and of knowledge producing firms. In a further step, we will adopt more advanced statistical techniques, i.e. latent class analysis, in order to provide a robust identification of classes of regions.
AB - In this paper, we develop an analysis of the spatial distribution of knowledge production related to Key Emerging Technologies (KETs) and Societal Grand Challenges (SGCs) in Europe building on an extensive dataset developed in the H2020 KNOWMAK project. We first provide a broad characterization of European regions in terms of their knowledge volume and knowledge intensity, which leads to a distinction between the large metropolitan regions and smaller knowledge intensive regions. Second, by using principal component analysis, we identify two components of knowledge production that we broadly characterize as academic production and technology production. This distinction allows further categorizing regions in terms of the balance between the two components, which we suggest is also related to the ecology of actors in a region and, notably, of the importance of public-sector research and of knowledge producing firms. In a further step, we will adopt more advanced statistical techniques, i.e. latent class analysis, in order to provide a robust identification of classes of regions.
UR - http://www.scopus.com/inward/record.url?scp=85076913996&partnerID=8YFLogxK
M3 - Conference contribution
T3 - 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings
SP - 685
EP - 690
BT - 17th International Conference on Scientometrics and Informetrics, ISSI 2019 - Proceedings
A2 - Catalano, Giuseppe
A2 - Daraio, Cinzia
A2 - Gregori, Martina
A2 - Moed, Henk F.
A2 - Ruocco, Giancarlo
PB - International Society for Scientometrics and Informetrics
T2 - 17th International Conference on Scientometrics and Informetrics
Y2 - 2 September 2019 through 5 September 2019
ER -