Spatial analysis of the smart specialization potential of Russian regions: identifying trans-regional growth centers

Objective: to develop and test a methodology for identifying trans-regional growth centers based on smart specialization. This allows identifying territorial clusters with high innovation potential and spatial patterns of interregional interaction in the context of ensuring Russia’s technological sovereignty.

Methods: the study is based on a comprehensive analysis of official statistics and expert assessments. The methods of confirmatory factor analysis were used to calculate the integral index of smart specialization potential, which takes into account scientific potential, educational infrastructure, digitalization, innovation activity, R&D financing, labor market, foreign economic activity, and government regulation. The global and local Moran indices, as well as the “hot points” method, were used to estimate spatial autocorrelation.

Results: significant spatial autocorrelation between the regions of Russia was revealed (global Moran index – 0.457). The author identified 12 first-order transregional growth centers (Perm region, St. Petersburg, Moscow, Ulyanovsk, Samara, Tomsk regions, Republic of Tatarstan, Nizhny Novgorod, Kaluga, Moscow, Sverdlovsk, Novosibirsk regions), 12 second-order centers and 28 territories in their zone of influence, forming the core of smart specialization in Russia. It was determined that the most stable territorial clusters appear when regions with different types of industry specialization are integrated. For individual growth centers, a “shadow effect” was identified that prevents the diffusion of innovations and technology transfer.

Scientific novelty: the author developed a methodology that combines the analysis of individual characteristics of regional innovation systems and network effects of territorial interactions. For the first time, an integrated approach to the assessment of smart specialization was applied, taking into account interregional relations, which contributes to the formation of an adaptive regional policy.

Practical significance: the methodology allows differentiating state support measures for territorial clusters. The results can be used in the elaboration of territorial development strategies and regional innovation growth programs. The identified growth centers serve as the basis for coordinating interregional projects and optimizing resource allocation.

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