ТИПОЛОГИЯ
РЕГИОНОВ ПО ИХ ФУНКЦИОНАЛЬНОЙ РОЛИ В ИННОВАЦИОННОМ ПРОСТРАНСТВЕ РОССИИ
Title |
Typology of regions by functional
role in Russian innovation space |
Автор(ы) |
А.
С. Михайлов, Т. Ю. Кузнецова, И. Ю. Пекер |
Author(s) |
S. Mikhaylov,
T. Yu. Kuznetsova, I. Yu. Peker |
DOI |
|
Страницы/Pages |
46-57 |
Статья |
|
Ключевые
слова |
инновационное
пространство, география инноваций, интеллектуальный капитал,
научно-технологический потенциал, приморский фактор, приморский регион. |
Keywords |
innovative space, innovation geography,
intellectual capital, scientific and technological potential, coastal factor,
coastal region. |
Аннотация |
Статья
посвящена исследованию неоднородности инновационного пространства России в
аспекте оценки территориального распределения и концентрации центров создания
нового знания. В качестве рабочей гипотезы принято предположение о
существовании регионов трех типов: полюсов роста - крупнейших городов и
агломераций, генерирующих значительный объем ново го знания широкой
специализации, зон влияния - территорий с высоким генеративным потенциалом в
одной или нескольких специфических отраслях знания, и инновационной
периферии, демонстрирующей слабую способность к созданию нового знания.
Результатом анализа показателей публикационной активности в 2013-2017 гг. в
разрезе субъектов РФ методом пространственной наукометрии
стало уточнение и детализация первоначальной типологии регионов по их
генеративной функции в национальном инновационном процессе. Исследование
показало значимость комплекса пристоличного,
приморского и приграничного факторов в реализации инновационного потенциала
территории. Для приморских регионов, в т.ч. имеющих приграничное положение,
характерен более высокий уровень научной продуктивности и интеграции в
международное научно-техническое сотрудничество, а также общая положительная
динамика прироста нового научного знания. Кроме того, приморский фактор
обусловливает специфику тематической направленности создаваемого интеллектуального
капитала и базы знаний, накапливаемой в регионе. |
Abstract (summary) |
The article is devoted to studying
the spatial heterogeneity of innovation space across Russia while assessing
the regional divergence and concentration of knowledge-generating centers.
The preliminary hypothesis suggests there are three types of regions: growth
poles - the largest cities and agglomerations that generate a significant
amount of new knowledge of wide specialization spectrum; zones of influence -
territories with high generative potential in one or several specific
knowledge domains; and innovation peripheries that demonstrate weak ability
to generate new knowledge. The analysis of an array of indicators on
publication activity over 2013-2017 across the regions of the Russian
Federation using the method of spatial scientometrics
has clarified and detailed the initial typology of regions according to their
generative function in the interregional innovation process. The study showed
the importance of a complex of metropolitan, coastal and border factors
affecting the innovative potential of territories. For coastal regions, incl.
having a cross-border position, a higher level of research productivity and
integration into international S&T cooperation is characteristic, as well
as a general positive dynamics of new knowledge generation if found. In
addition, the coastal factor determines the specificity of the subject area
of the intellectual capital created and the knowledge base accumulated in the
region. |
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Knowledge-Based Development, 2016, no. 7 (1), pp. 4–21.
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Regional divergence dynamics in the Baltic region: Towards polarisation or equalization? Geographia
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F., Verburg T. Related Variety, Unrelated Variety
and Regional Economic Growth. Regional Studies, 2007, no. 41 (5),
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regions. European Planning Studies, 2018, no. 26 (8), pp.
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J., Chen K. Measuring the innovation production process: A cross-region
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international comparison of relative contributions to academic
productivity. Scientometrics, 2009, no.
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an analysis and visualization of publication activity and research
collaboration at the country level. Quality and Quantity, 2019,
no. 53 (4), pp. 2143–2173. https://doi.org/10.1007/s11135-019-00863-z. 29. Kotsemir M., Shashnov S.
Measuring, analysis and visualization of research capacity of university at
the level of departments and staff members. Scientometrics,
2017, no. 112 (3), pp. 1659–1689. https://doi.org/10.1007/s11192-017-2450-7. 30. Mikhaylov A.S. Coastal agglomerations and the
transformation of national innovation spaces. Baltic Region,
2019, vol. 11, no. 1, pp. 29–42. https:// doi.org/10.5922/2078-8555-2019-1-3. 31. Mikhaylov A.S., Peker I.Yu. Spatial Distribution of the Intellectual Capital of
Russia. Vysshee Obrazovanie
v Rossii, 2019, no. 28 (6), pp. 28–39. 32. Mikhaylov A., Kuznetsova T. Y.,
Peker I. Y. Knowledge geography: Human geography
approach to measuring regional divergence of knowledge capital. Paper
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Management (ECKM), 2019, vol. 2, pp. 738-745. https://doi.
org/10.34190/KM.19.239. 33. Mikhaylova A.A., Mikhaylov A.S.
Re-distribution of knowledge for innovation around Russia. International
Journal of Technological Learning, Innovation and Development, 2016, no.
8 (1), pp. 37-56. 34. Shashnov S., Kotsemir M.
Research landscape of the BRICS countries: current trends in research output,
thematic structures of publications, and the relative influence of
partners. Scientometrics, 2018, no. 117
(2), pp. 1115–1155. https://doi.org/10.1007/s11192-018-2883-7. 35. Sooryamoorthy R. Scientific knowledge in South Africa:
information trends, patterns and collaboration. Scientometrics,
2019, no. 119 (3), pp. 1365–1386. https://doi.org/10.1007/s11192-019-03096-x. 36. Zemtsov S., Kotsemir M. An
assessment of regional innovation system efficiency in Russia: the
application of the DEA approach. Scientometrics,
2019, no, 120 (2), pp. 375–404. https://doi. org/10.1007/s11192-019-03130-y. 37. Zemtsov S.P., Baburin V.L., Kidyaeva V.M. Innovation Clusters and Prospects for
Environmental Management in Russia. Geography and Natural Resources,
2018, no. 39 (1), pp. 10–15. 38. Zuo K., Guan J. Measuring the R&D efficiency of
regions by a parallel DEAgame model. Scientometrics, 2017, no. 112 (1), pp. 75–194.
https://doi.org/10.1007/s11192-017-2380-4. |