Innovation potential of regions in Northern Eurasia.

• Main Authors: Zemtsov, Stepan, Baburin, Vyacheslav
• Format: Other/Unknown Material
• Language: English
• Published: Louvain-la-Neuve: European Regional Science Association (ERSA) 2013
• Subjects: ddc:330; Russia; regions; innovation; potential; clusters; northern communities; demo; geo; Arctic; Magadan; Sakha
• Online Access: http://hdl.handle.net/10419/123949

Northern territories (including the Arctic) occupy over 80% of Russian area. Development of these regions is based on ?resource? model, while other approaches have been ignored because of severe environmental conditions. The aim of this study was to assess an ability of northern regions to generate and diffuse innovations. The study was methodologically divided into three stages. The objective of the first and the second stage was to compare innovation capacities of northern and other Russian regions. An ability to create new knowledge is described by a number of indexes, the ability to extend and apply innovations - by a logistic function from model for innovation diffusion. This work confirmed the hypothesis of high concentration of the potential in major agglomerations and research centers, including Siberian cities: Tomsk, Novosibirsk, and Krasnoyarsk. Some arctic regions were characterized by high creative potential, but low rate of diffusion: Krasnoyarsk, Magadan, Sakha. The first fact can be explained by conservation of the Soviet scientific infrastructure and by initiative and mutual assistance of northern communities. The second fact is related to low population density and interaction. The key disadvantage of the method is in inadequate quality of Russian statistics. On the second stage, the authors identified innovation clusters in the sphere of environmental management. This sphere, connected with sustainable development, is a quickly developing innovative sector of economy, which includes remote sensing and GIS technologies, new technologies of exploration, hydro-meteorological and ecological modeling, etc. Leading university centers were identified by expert surveys and verified by ?Delphi? procedures. Centers had formed clusters, which were organized by principal of innovation cycle: fundamental and applied science, and enterprises. More than 30% of organizations were located in the northern regions. To classify the clusters the authors calculated an index of innovation capacity, which included ...