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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">vestrea</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник Российского экономического университета имени Г. В. Плеханова</journal-title><trans-title-group xml:lang="en"><trans-title>Vestnik of the Plekhanov Russian University of Economics</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2413-2829</issn><issn pub-type="epub">2587-9251</issn><publisher><publisher-name>Plekhanov Russian University of Economics</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.21686/2413-2829-2023-3-29-39</article-id><article-id custom-type="elpub" pub-id-type="custom">vestrea-1648</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>МАТЕМАТИЧЕСКИЕ, СТАТИСТИЧЕСКИЕ И ИНСТРУМЕНТАЛЬНЫЕ МЕТОДЫ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>MATHEMATIC, STATISTICAL AND INSTRUMENTAL METHODS</subject></subj-group></article-categories><title-group><article-title>Знание и пространство в моделях регионального развития</article-title><trans-title-group xml:lang="en"><trans-title>Knowledge and space in models of regional development</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2639-498X</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Мельникова</surname><given-names>Т. Б.</given-names></name><name name-style="western" xml:lang="en"><surname>Melnikova</surname><given-names>T. B.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Татьяна Борисовна Мельникова кандидат экономических наук, доцент кафедры менеджмента, туризма и гостиничного бизнеса</p><p>299053, Севастополь, ул. Вакуленчука, д. 29</p></bio><bio xml:lang="en"><p>Tatyana B. Melnikova PhD, Assistant Professor of the Department for Management, Tourism and Hospitality</p><p>29 Vakulenchuka Str., Sevastopol, 299053</p></bio><email xlink:type="simple">tmln82@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Севастопольский филиал Российского экономического университета имени Г. В. Плеханова</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Sevastopol Institute (branch) of the Plekhanov Russian University of Economics</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2023</year></pub-date><pub-date pub-type="epub"><day>19</day><month>06</month><year>2023</year></pub-date><volume>0</volume><issue>3</issue><fpage>29</fpage><lpage>39</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Мельникова Т.Б., 2023</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="ru">Мельникова Т.Б.</copyright-holder><copyright-holder xml:lang="en">Melnikova T.B.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://vest.rea.ru/jour/article/view/1648">https://vest.rea.ru/jour/article/view/1648</self-uri><abstract><p>Новое знание воспринимается как важный ресурс положительной динамики развития территории. В результате знание становится частью экономических моделей, которые пытаются обобщить закономерности экономического роста и развития регионов, городов и стран. В статье представлен структурированный анализ наиболее известных моделей для исследования регионального развития, сочетающих факторы знания и пространства. Рассмотрены способы включения знания и пространства в модели, где объектом наблюдения выступают территориальная социально-экономическая система, предприятие или агент, владеющий знаниями. Представлены модели равновесного роста, эконометрические, а также с задачей максимизации. Также применяются пространственный автокорреляционный анализ и пространственные регрессионные модели, которые позволяют оценить неоднородность инновационного развития регионов с выделением зон влияния. В результате выявлены способы моделирования общедоступных (отраслевых) знаний для разного уровня территорий, глобальных и локализованных внешних эффектов, географического совпадения разного типа исследований, максимизации исследований через альтернативные издержки, монопольных отраслевых эффектов, влияния отраслевой конкуренции и разнообразия, а также «фильтра» знаний. Пространственные особенности распространения знаний моделируются посредством функций активности и сопротивления, параметра эластичности, функции затухания расстояния, параметра транзакционных затрат на перемещение знаний.</p></abstract><trans-abstract xml:lang="en"><p>New knowledge is recognized as an important resource of positive dynamics of the territory development. As a result knowledge becomes an element of economic models that try to summarize regularities of economic growth and development of regions, cities and countries. The article provides structured analysis of the well known models to research regional development, which combines factors of knowledge and space. It studies ways of including knowledge and space in models, where the object of observation is territorial social and economic system, enterprise or agent possessing knowledge. Models of balanced growth, econometric ones and models with maximization aim are shown. At the same time space autocorrelation analysis and space regressive models can be used that help assess heterogeneity of innovation development in regions and identify influence zones. As a result methods of modeling general (industry) knowledge for territories with different level, global and local external effects, geographic identity of research of different types, maximization of research through alternative costs, monopoly industrial effects, impact of industrial competition and diversity, as well as knowledge ‘filter’ are revealed. Space characteristics of knowledge proliferation can be modeled through functions of activity and resistance, flexibility parameter, function of distance fading, parameter of transaction costs for knowledge transfer.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>знание</kwd><kwd>экономическая модель</kwd><kwd>локализованные внешние эффекты от создания знаний</kwd><kwd>регион</kwd><kwd>город</kwd></kwd-group><kwd-group xml:lang="en"><kwd>knowledge</kwd><kwd>economic model</kwd><kwd>localized external effects of knowledge creation</kwd><kwd>region</kwd><kwd>city</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Наумов И. В., Барыбина А. З. Пространственная регрессионная модель инновационного развития регионов России // Вестник Томского государственного университета. 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