Pегионы России: результаты кластеризации на основе экономических и инновационных показателей

Currently, one of the main trends is the study of the features and benefits of regional development, increasing the importance of the role of regions in national and world politics. The differences in technological results that can be observed at the national and regional levels are largely due to the peculiarities of the institutional environment, i.e. the degree of concentration at the regional level of high-tech companies, modern production and innovation infrastructures. The regions of the Russian Federation demonstrate noticeable differences regarding the level of socio-economic development, the availability of human and natural resources, the development of educational, scientific and innovative potentials, depending on the historical development of infrastructure. This study examines the results of clustering Russian regions according to the main indexes characterizing the economic, scientific and innovative activity. The classification of regions was carried out by the method of cluster analysis.Purpose of the study. The aim of the study was to identify homogeneous groups of regions that are similar in their economic and innovation indexes, statistical analysis of these groups based on non-parametric methods and methods of correlation and regression analysis, the formation of conclusions and recommendations regarding innovation.Materials and methods. The information base of the study was statistical data and analytical information characterizing the state of economic and innovation activity in the Russian regions. The following statistical methods were used in the study: non-parametric (Spearman’s rank correlation coefficients, Mann-Whitney test), correlation (Pearson’s coefficients, coefficients of determination), regression (non-linear regression models), multivariate classifications (cluster analysis), descriptive statistics (averages, structural averages, indicators of variation, etc.).Results. As a result of clustering the regions of Russia using the k-means method, 4 cluster groups were obtained, which are statistically homogeneous within the studied indexes. In order to identify the relationships between the considered indexes, paired linear Pearson correlation coefficients were calculated. The study tested three hypotheses about statistically significant differences between the indexes of the third and fourth clusters. The set of indexes was as follows: the coefficient of inventive activity, internal costs of research and development per employee, the average per capita size of innovative goods and services. For these purposes, the nonparametric Mann-Whitney test was used. The analysis showed that the regions of the Russian Federation are extremely diverse and heterogeneous in terms of their economic and innovative development. When analyzing them, it is advisable to first use cluster analysis methods to obtain homogeneous groups of territories with similar social and economic characteristics, which is confirmed in this study by testing hypotheses about statistically significant differences between the indexes of the third and fourth clusters (differences between the first and second clusters with other clusters and between themselves obvious and do not require any mathematical proof).Conclusion. The leaders in scientific and innovative development are Moscow, St. Petersburg, the Moscow region and the Republic of Tatarstan. They have the highest rates of inventive activity of the population and the volume of production of innovative goods and services. Such regions of the Russian Federation as the Tyumen region, the Republic of Sakha (Yakutia), Magadan region, Sakhalin region and Chukotka formed a cluster group with the highest per capita GRP, investments and fixed assets, but they have almost the lowest rates of innovation activity. The extractive industry is the main engine of the economy of these regions. A separate cluster was formed by 26 regions with average levels of economic and innovative development in the Russian Federation. In particular, it includes the areas: Belgorod, Lipetsk, Smolensk, Arkhangelsk, Vologda, Leningrad, Murmansk, Chelyabinsk, Irkutsk, Tomsk, etc. These regions are promising in terms of innovation, but require significant federal investments for their further development. The fourth group of regions united economically weak territories with low rates of innovation activity. These regions accounted for more than half of the total (47 regions). Statistical analysis within the resulting clusters made it possible to identify the relationship between economic indexes and describe them using regression models.В настоящее время одним из основных трендов является изучение особенностей и преимуществ регионального развития, повышение значимости роли регионов в национальной и мировой политике Имеющиеся различия в технологических результатах, которые можно наблюдать на национальном и региональном уровнях, в значительной степени обусловлены особенностями институциональной среды, т.е. степенью концентрации на уровне региона высокотехнологичных компаний, современной производственной и инновационной инфраструктур. Регионы Российской Федерации демонстрируют заметные различия, касающиеся уровня социально-экономического развития, наличия человеческих и природных ресурсов, развития образовательного, научного и инновационного потенциалов в определенной зависимости от исторически сложившейся развитости инфраструктуры. В  данном исследовании рассматриваются результаты кластеризации российских регионов по основным показателям, характеризующим экономическую, научную и инновационную деятельность. Классификация регионов осуществлялась методом кластерного анализа. Цель исследования. Целью исследования являлось определение однородных групп регионов, схожих по своим экономическим и инновационным показателям, статистический анализ этих групп на основе непараметрических методов и методов корреляционно-регрессионного анализа, формирование выводов и рекомендаций, касающихся инновационной деятельности.Материалы и методы. Информационной базой исследования послужили статистические данные и аналитическая информация, характеризующая состояние экономической и инновационной деятельности в российских регионах. В исследовании использовались следующие статистические методы: непараметрические (ранговые коэффициенты корреляции Спирмена, критерий Манна Уитни), корреляционный (коэффициенты Пирсона, коэффициенты детерминации) регрессионный (нелинейные регрессионные модели), многомерные классификации (кластерный анализ), описательные статистики (средние, структурные средние, показатели вариации и др.).Результаты. В результате кластеризации регионов России методом k-средних получены 4 кластерных группы, внутри статистически однородные по исследуемым показателям. С целью выявления взаимосвязей между рассматриваемыми показателями рассчитывались парные линейных коэффициенты корреляции Пирсона. В ходе исследования были проверены три гипотезы о статистически значимых различиях между показателями третьего и четвертого кластеров. Набор показателей был следующий: коэффициент изобретательской активности, внутренние затраты на исследования и разработки в расчете на одного работника, среднедушевой размер инновационных товаров и услуг. Для этих целей был использован непараметрический критерий Манна-Уитни. Проведенный анализ показал, что Регионы РФ крайне разнообразны и неоднородны по своему экономическому и инновационному развитию. При их анализе целесообразно предварительно использовать методы кластерного анализа для получения однородных групп территорий со схожими социальными и экономическими характеристиками, что подтверждается в настоящем исследовании проверкой гипотез о статистически значимых различиях между показателями третьего и четвертого кластеров (различия первого и второго кластеров с остальными кластерами и между собой очевидны и не требуют каких-либо математических доказательств).Заключение. Лидерами в научном и инновационном развитии являются г. Москва, г. Санкт-Петербург, Московская область и Республика Татарстан. У них самые высокие показатели изобретательской активности населения и объемы производства инновационных товаров и услуг. Такие субъекты РФ, как Тюменская область, республика Саха (Якутия), Магаданская область, Сахалинская область и Чукотка образовали кластерную группу с самыми высокими размерами среднедушевых ВРП, инвестиций и основных фондов, но у них практически самые низкие показатели инновационной активности. Добывающая промышленность является главным двигателем экономики этих регионов. Свой отдельный кластер образовали 26 регионов со средними по РФ уровнями экономического и инновационного развития. В частности, в него вошли области: Белгородская, Липецкая, Смоленская, Архангельская, Вологодская, Ленинградская, Мурманская, Челябинская, Иркутская, Томская и др. Эти регионы перспективны в инновационном плане, но требуют для своего дальнейшего развития существенных федеральных вложений. Четвертая группа регионов объединила экономически слабые территории с низкими показателями инновационной деятельности. Эти регионы составили более половины от всей совокупности (47 регионов). Статистический анализ внутри полученных кластеров позволил выявить взаимосвязи экономических показателей и описать их с помощью регрессионных моделей

An exposure-effect approach for evaluating ecosystem-wide risks from human activities

Ecosystem-based management (EBM) is promoted as the solution for sustainable use. An ecosystem-wide assessment methodology is therefore required. In this paper, we present an approach to assess the risk to ecosystem components from human activities common to marine and coastal ecosystems. We build on: (i) a linkage framework that describes how human activities can impact the ecosystem through pressures, and (ii) a qualitative expert judgement assessment of impact chains describing the exposure and sensitivity of ecological components to those activities. Using case study examples applied at European regional sea scale, we evaluate the risk of an adverse ecological impact from current human activities to a suite of ecological components and, once impacted, the time required for recovery to pre-impact conditions should those activities subside. Grouping impact chains by sectors, pressure type, or ecological components enabled impact risks and recovery times to be identified, supporting resource managers in their efforts to prioritize threats for management, identify most at-risk components, and generate time frames for ecosystem recovery

An environmental assessment of risk in achieving good environmental status to support regional prioritisation of management in Europe

The Marine Strategy Framework Directive (MSFD) aims to achieve Good Environmental Status (GES) in Europe's Seas. The requirement for regional sea authorities to identify and prioritise issues for management has meant that standardized methods to assess the current level of departure from GES are needed. The methodology presented here provides a means by which existing information describing the status of ecosystem components of a regional sea can be used to determine the effort required to achieve GES. A risk assessment framework was developed to score departure from GES for 10 out of the 11 GES descriptors, based on proposed definitions of 'good' status, and current knowledge of environmental status in each of the four regional seas (North-East Atlantic, Mediterranean Sea, Baltic Sea and Black Sea). This provides an approach for regional evaluation of environmental issues and national prioritisation of conservation objectives. Departure from GES definitions is described as 'high', 'moderate' or low' and the implications for management options and national policy decisions are discussed. While the criteria used in this study were developed specifically for application toward MSFD objectives, with modification the approach could be applied to evaluate other high-level social, economic or environmental objectives. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved

Report on identification of keystone species and processes across regional seas. DEVOTES FP7 Project

WP6, Deliverable 6.1, DEVOTES ProjectIn managing for marine biodiversity, it is worth recognising that, whilst every species contributes to biodiversity, each contribution is not of equal importance. Some have important effects and interactions, both primary and secondary, on other components in the community and therefore by their presence or absence directly affect the biodiversity of the community as a whole. Keystone species have been defined as species that have a disproportionate effect on their environment relative to their abundance. As such, keystone species might be of particular relevance for the marine biodiversity characterisation within the assessment of Good Environmental Status (GEnS), for the Marine Strategy Framework Directive (MSFD).The DEVOTES Keystone Catalogue and associated deliverable document is a review of potential keystone species of the different European marine habitats. The catalogue has 844 individual entries, which includes 210 distinct species and 19 groups classified by major habitat in the Baltic Sea, North East Atlantic, Mediterranean, Black Sea (EU Regional Seas) and Norwegian Sea (Non-­‐EU Sea). The catalogue and the report make use/cite 164 and 204 sources respectively. The keystones in the catalogue are indicated by models, by use as indicators, by published work (e.g. on traits and interactions with other species), and by expert opinion based on understanding of systems and roles of species/groups. A total of 74 species were considered to act as keystone predators, 79 as keystone engineers, 66 as keystone habitat forming species, while a few were thought of having multiple roles in their marine ecosystems. Benthic invertebrates accounted for 50% of the reported keystone species/groups, while macroalgae contributed 17% and fish12%. Angiosperms were consistently put forward as keystone habitat forming and engineering species in all areas. A significant number of keystones were invasive alien species.Only one keystone, the bivalve Mya arenaria, was common to all four EU regional seas. The Mediterranean Sea had the largest number of potential keystones (56% of the entries) with the least in the Norwegian Sea. There were very few keystones in deep waters (Bathyal-­‐Abyssal, 200+ m), with most reported in sublittoral shallow and shelf seabeds or for pelagic species in marine waters with few in reduced/variable salinity waters. The gaps in coverage and expertise in the catalogue are analysed at the habitat and sea level, within the MSFD biodiversity component groups and in light of knowledge and outputs from ecosystem models (Ecopath with Ecosim).The understanding of keystones is discussed as to when a species may be a dominant or keystone with respect to the definition term concerning ‘disproportionate abundance’, how important are the ‘disproportionate effects’ in relation to habitat formers and engineers, what separates a key predator and key prey for mid-­‐trophic range species and how context dependency makes a species a keystone. Keystone alien invasive species are reviewed and the use of keystone species model outputs investigated. In the penultimate sections of the review the current level of protection on keystone species and the possibilities for a keystone operational metric and their use in management and in GEnS assessments for the MSFD are discussed. The final section highlights the one keystone species and its interactions not covered in the catalogue but with the greatest impact on almost all marine ecosystems, Homo sapiens

Light absorption and phytoplankton biomass in the Black Sea in spring 1995

Spectra of light absorption by suspended matter, phytoplankton, and detritus in the central and coastal parts of the Black Sea over the spring period (March-April 1995) were determined. Vertical homogeneity of the upper 40 m layer with respect to parameters in study was noted. Value of light absorption by phytoplankton normalized with respect to chlorophyll a was virtually independent of chlorophyll a concentration. A linear relationship between light absorption by phytoplankton and chlorophyll a concentration was established at the red spectral maximum. It is described by the equation y = 0.0153x; R**2 = 0.61. The average ratio of absorption values in the peaks was 2.29. Contribution of detritus to total light absorption at wavelength 440 nm was 23-62% regardless of depth and chlorophyll a concentration

Фотосинтетические характеристики фитопланктона в западной части Чёрного моря в период осеннего цветения

Фотосинтетические характеристики фитопланктона были исследованы вдоль разрезов от берега в глубоководную часть в западном и южном районах Черного моря осенью в 2005 г. Эффективность фотосинтеза (αB) изменялась от 0.012 до 0.068 мг C (мг Chl)-1ч-1(μE м-2 с-1)-1, а максимальная скорость фотосинтеза, нормированная на хлорофилл а (P), - от 5 мг C (мг Chl)-1ч-1 в глубоководной части до 12 мг C (мг Chl)-1ч-1 в шельфовых водах. В районе шельфа величины P и αB изменялись в 3 раза. Степень вариабельности этих параметров была такой же, как и концентрации хлорофилла. Средние величины αB и P на шельфе превышали значения для глубоководного района. Стратегия фотоадаптации фитопланктона заключалась в повышении αB c уменьшением освещенности. Осенью биогенные вещества определяли изменчивость фотосинтетических параметров. Величины интегральной первичной продукции (PP)изменялись от 0.34 до 2.45 гC м-2 сут-1, в среднем - 1.7 и 0.5 гC м-2 сут-1 в шельфовом и глубоководном районах, соответственно. Найдены зависимости РР, P и αB от поверхностной концентрации хлорофилла а.In the autumn of 2005 photosynthetic characteristics of phytoplankton were studied along transects from a coast to deep-waters in the western and southern Black Sea. Efficiency of photosynthesis (αB) varied from 0.012 to 0.068 mg C (mg Chl)-1h-1(μE m-2 s-1)-1. Light – saturated rate of chlorophyll a-normalized photosynthesis (P) ranged from 5 mg C (mg Chl)-1 h-1 in the deep-waters to 12 mg C (mg Chl)-1 h-1 in the shelf waters. In the shelf area P and αB varied by a factor of 3. Variability of these parameters was as lower as that of the chlorophyll concentration. The mean αB and P values at the shelf were higher than those of the deep-water region. The phodoadaptive strategy of phytoplankton was characterized by increasing of αB at lower light levels. During the warm period of year nutrient effect on photosynthetic parameters was critical in their variability. The total integrated primary production (PP) varied from 0.34 to 2.45 gC m-2 day-1. Mean PP values were 1.7 and 0.5 gC m-2 day-1 in the shelf and deep-waters respectively. Strong correlations between PP, P, αB and surface chlorophyll concentration have been obtained