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Gavrilenko D.V. lecturer, All-Russian State Tax Academy of Ministry of Financein the Russian Federation
Journal: Economic Analysis: Theory and Practice, #24,
The article is dedicated to analysis of practical taxation problems related to multi-component complex ore, non-ferrous and ferrous metals’ mining. Consideration is made for present existing practice regarding taxation for mentioned mineral resources’ mining. Determination is made for reasons of contradictions in selection of tax project and tax base computation regarding tax for minerals resources’ mining. Emphasis is made on necessary correspondence of tax computation procedure to economic meaning. As a result, identification was made for three groups of existing problems, requiring quick legislative decision.
Gavrilenko D.S. Institute of Economics and Management of Kemerovo State University (KemSU), Kemerovo, Russian Federation ( Vanghelsons10@yandex.ru )
Kalacheva I.V. Institute of Economics and Management of Kemerovo State University (KemSU), Kemerovo, Russian Federation ( firstname.lastname@example.org )
Journal: Regional Economics: Theory and Practice, #2,
Subject This article examines the state-of-the-art of agriculture and agricultural product market of the Russian Federation and the Kemerovo oblast, in particular.
Objectives The article aims to define the range of the main problems of industry development and develop measures to improve the conditions of agricultural sector development both at the country level and within a specific region.
Methods For the study, we used the methods of logical and statistical analyses.
Results The article identifies key factors and major trends in, as well as financial and administrative barriers to the development of the agricultural sector.
Conclusions The main problem for the effective development of agriculture is a low level of investment in the industry due to the high risks associated with seasonality of production, climatic and natural conditions. The solutions proposed in the article will allow for the modernization of production in the face of a cash deficit and contribute to an increase in investment flows.
Gavrilenko M.A. Lomonosov Moscow State University, Moscow, Russian Federation ( email@example.com )
Journal: Financial Analytics: Science and Experience, #45,
Importance One of the most crucial priorities of business development under the current changing economic environment is developing and implementing an efficient risk analysis system. Implementing new approaches to risk management can lead to substantial reduction in probability of losses from investment activity.
Objectives The article deals with developing an algorithm of comparing investment projects by risk level. The tasks were as follows: application of the qualitative risk analysis at the risk identification stage; application of the generalized fuzzy sets theory to the risk assessment procedure; development (application) of an adequate mathematic methodology; possibility to implement the algorithm to a vast class of investment projects.
Methods I have developed an algorithm of comparing investment projects by risk level. This algorithm bases on the generalized fuzzy numbers theory, which reveals substantial advantages to an analyst through assessing risks of investment projects in conditions of limited information.
Results The proposed algorithm is universal because it can be implemented in many investment projects. The developed methodology is simple for understanding and convenient for practical implementation.
Conclusions and Relevance It is possible to apply the proposed algorithm to risk assessment together with generally accepted methods. This algorithm implies major job at the stage of risk factors identification. This is a great advantage as accurate and precise identification of risk factors leads to more precise risk assessment. The generalized fuzzy numbers theory enables to introduce expert judgment. This implies a certain level of flexibility of the proposed algorithm. The proposed methodology contributes to developing the theory of fuzzy sets and demonstrates the benefits of its use in relation to risk management issues.
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