Ключевые слова: регион, экономические факторы, развитие, инвестиции Аннотация Предмет. Определение направлений инвестиционных вложений в региональную экономику субъектов Федерации. Цели. Для определения направлений инвестиционных вложений следует выбрать регионы, способные в наибольшей степени эффективно реализовать поступающие финансовые средства для стимулирования реальных секторов экономики, формирования точек роста, которые могли бы повлечь за собой развитие региона в целом. Для решения данной проблемы необходимо разработать новый комплексный и точный инструментарий оценки потенциальных возможностей региональной экономической структуры. Методология. Предложенная методология и реализующий ее инструментарий базируются на комплексном использовании метода нейронных сетей применительно к экономике региона. Предлагается использовать региональные экономические показатели, определяющие результативную составляющую, представленную валовым региональным продуктом. При этом сами показатели выбраны таким образом, что они характеризуют экономическое развитие в наибольшей степени. Результаты. Для комплексной оценки эффективности инвестиционных вложений разработан метод, с помощью которого при ограниченном количестве исходных показателей, характеризующих экономическую деятельность региона, можно получить достаточно точные оценки результативности проводимой инвестиционной политики в отношении каждого субъекта Федерации. Перспективность данного подхода состоит в возможности моделирования регионального социально-экономического развития в ответ на возможные структурные изменения, происходящие в экономике региона и имеющие как внутренние, так и внешние причины. Предложенный метод позволяет определить наиболее целесообразное направление инвестиционных вложений для социальноэкономического развития регионов, осуществить задачу по сглаживанию пространственной поляризации, которая в настоящее время имеет тенденцию к увеличению. Выводы. Разработанный методический подход дает возможность оценить целесообразность кластерного объединения субъектов Российской Федерации и использования метода нейросетевого моделирования для совершенствования государственной инвестиционной политики.

Subject The research investigates that the Russian economy became less dependent on foreign imports of materials, spare parts, software and equipment needed for scienceintensive production. Objectives The article comprehensively studies the dependence of the Russian economy on imports, outlines proposals for making science-intensive enterprises less vulnerable to external factors. Methods The research is based on a systems method and methods of economic analysis. Results Having analyzed the dependence on imports, we revealed key types of negative factors posing technological threats to the innovative development of the national economy. They mainly arise in production management, creation of productive means, foreign imports of materials and components. The Russian machine building was found to be almost completely dependent on foreign high-precision mixed-use machines and respective components. The Russian radio-electronic industry lags behind the global technological progress, being yet unable to supply domestically produced electronic devices to the Russian enterprises. Some top priority enterprises have automated management systems which are run by foreign experts. The prevalence of foreign equipment, materials, devices, other components and software stems from the low demand of the Russian consumers. Due to some reasons, the Russian manufacturers have not yet been able to offer high quality products to the Russian consumers. Conclusions and RelevanceThe import substitution program has not yet worked as it was supposed to. In certain cases, replication of foreign equipment even aggravates the situation, inhibiting the technological development and advancement. We propose demand stimulation measures by leasing the equipment, subsidizing manufacturing enterprises and consumption of national products. Finance of researches into technological equipment, materials and new generation technologies deserves a special mention.

In this article we examine the risks of reducing the consumption of natural gas in the countries that are the largest exporters of Russian natural gas (Germany, Italy, Turkey, China) caused by the development of renewable energy. The forecast of natural gas consumption is built up to 2030 by extrapolating the trend of time series, while selecting the type of trend takes into account the S-shaped development of new energy technologies. Two scenarios are considered: the first involves the development of the electric power industry in the way of "business as usual," while the second takes into account the development of intelligent grids and the Internet of energy (IoE). The results show that the intensive development of renewable energy in combination with the digitalization of electric grids can create the most significant risks for the development of gas energy in Germany and Turkey. In Germany, these risks are determined to a greater extent by the desire of the authorities to maintain the achieved level of energy security, which will inevitably fall with a decrease in coal generation and an increase in the share of gas. In Turkey, these risks are determined by the purely technical and technological development of the country, its dynamics and nature.

Developing sustainable renewable energy projects involves complex decision-making processes. At present time planning and developing of renewable energy projects across the globe imply calculation and consideration of negative environmental effects at all stages of energy project life cycle. The aim of the paper is to develop an environmental effects evaluation methodology based on ecological impact categories through all the stages of lifecycle of renewable energy technologies. We used data envelopment analysis to calculate the efficiency score for each renewable energy technology. EcoInvent database has been chosen as a source of eco-indicators. We suppose the efficiency ratio will remain unchanged when transferring estimates of the life cycle of renewable energy facilities to another territory. This allows us to use data obtained in other regions of the world to extrapolate comparative assessments and make the deliberate choice of the most environmentally preferable technology. The input-oriented DEA modelling has demonstrated geothermal and biogas technologies are the most preferable from an environmental point of view with the highest possible score. The least effective technologies are both modifications of PV with the minimum efficiency score. The results of the presented work indicated that DEA showed great promise to be an effective evaluating tool for future analysis on energy policy issues.

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