2020 Issue №2

The issue of regional development involves the study of growth drivers in a competitive, innovative and open environment. In these conditions, cluster forms of integration are considered as centers of economic and innovative re­gional development. The article examines a specialized agro-industrial com­plex that is developing at the interregional level on the coast of the Azov and Black Seas — in the Krasnodar Territory and the Rostov Region. The aim of the work is to analyze the actors, prerequisites and trajectory of the cluster-genesis process, factors of its acceleration and inhibition. The study focuses on the comparative efficiency of cluster forms for the development of the agro-industrial complex in the two regions in the global competition context. Given the methodological approaches to classical cluster studies, empirical material of both all-Russian and regional statistics, and data from real sector compa­nies operating in the regions under study, the authors formed a bi-component model of cluster development for the agro-industrial complex (including the "completion" of basic spheres in the agro-industrial complex and the actual cluster development formats). Today the innovative vectors of the complex de­velopment often lay along the large agricultural holdings, which also act as customers of R&D in the industry. The lack (despite the presence of formal strategies for regional and cluster development) of consensus on interaction in the industry and its development strategy was revealed. The cluster develop­ment component runs some certain risks in regulations. The authors indicate actual vectors of the development process. The obtained results can be used in working out the mechanisms for regional economic policy in the agro-industrial clustering.

When knowledge is a paramount resource, the effective management of its accumulation, use, and transfer becomes the most important function, which enables to assess and monitor the ability of territorial innovation sys­tems to generate new scientific knowledge. While the commercialization of knowledge in the territorial context is primarily considered at the regional level as part of the production process, it is more appropriate to consider the knowledge generation at the level of a city where the city is the most im­portant urbanized scientific regional centre. This study aims to assess the ter­ritorial distribution of Russian cities by their ability to generate new scientific knowledge with the subsequent development of recommendations for manag­ing their territorial capital as the basis of competitiveness. For Russia, the so­lution to this problem is most relevant in the view of a wide variety of cities and the impossibility and even danger of using unified approaches to their de­velopment. The large territory of the country, the remoteness of individual cit­ies from each other, their diversity in size, development level, knowledge spe­cialization makes it important to manage the diversification of knowledge. In this study, a science-related approach was used to assess the knowledge gener­ation based on the publication activity analysis of 2013—2017 according to the international abstract database of Scopus scientific citation. The study in­cludes 440 cities with at least 1 publication. Based on the results of the study, Russian cities fall into three types and individual subtypes, considering their specific features. The first type of cities “above average” is specified with high indicators of academic productivity, while for the first subtype they are sup­plemented by high indicators of scientific connectivity, citation and quality of publications. For the second subtype, these indicators are significantly lower. The second type of cities, “medium” or “transitional” have significant publi­cation experience. However, the insufficient average citation level and very few publications in high-ranking journals do not allow them to be attributed to the leading national scientific centres of the first group. The third type “be­low average” with the lowest values of all indicators is divided into three sub­types: the first group includes cities whose scientific groups publish a small number of articles with a high level; the second group brings together the cit­ies with a low level of scientific productivity having scientific connections at the national and international level; the third, the “silent” cities, which do not show any activity in any way.