Plekhanov Russian University of Economics, Moscow, Russia:

A. L. Novoselov, Professor, Doctor of Economic Sciences, alnov2004@yandex.ru

I. Yu. Novoselova, Associate Professor, Doctor of Economic Sciences

Financial University under the Government of the Russian Federation, Moscow, Russia:
I. V. Petrov, Professor, Doctor of Economic Sciences

The development of a mineral field requires significant capital investment and is characterized by long payback periods; therefore, one of the problems is the justification of the investment attractiveness of mining projects. Valuation of mineral deposits plays an important role in solving a variety of management tasks: holding a tender (auction) and issuing licenses; justification of investments for development of deposits; determining the order of their development; assessment of damage and loss of profits; resolution of property disputes, etc. The cost of a mineral deposit depends on the aggregate of geological, geological, and organizational and economic factors. The article considers the task of assessing the priority of incentive measures that ensure the change in organizational and economic factors aimed at increasing the value of a mineral deposit. The authors presented a formula for estimating the value of a field, including the change in individual components of the cash flow through the implementation of measures to stimulate its growth. The evaluation of the preference for incentive measures was proposed to be carried out simultaneously using two criteria: changes in the value of the field as a result of the implementation of incentive measures and an assessment of the success of each incentive measure under consideration. To assess the priority of measures to stimulate rise in the value of a field, an algorithm has been developed based on a fuzzy expert assessment of the probability of successful implementation of the incentive measures under consideration; calculating a fuzzy estimate of the growth in the value of a field based on discounted cash flows when implementing incentive measures. Such a multicriteria assessment allows a comprehensive assessment of the priority of measures to stimulate the growth of the value of a mineral deposit. The article presents a modification of the method of pairwise comparisons, which allows the use of the developed fuzzy criteria. As a result of the use of the proposed algorithm, an estimate of the priority of the incentive measures under consideration is found from 0 to 1. The proposed algorithm for evaluating measures to stimulate the growth of the value of deposits is tested on the example of a copper ore deposit.

1. Petrov O. V. (Ed.). Mineral Wealth of the Russian Federation. In two volumes. Saint-Petersburg : VSEGEI, 2009. Vol. 2: Analysis of Mineral Reserves and Costs. 491 p.
2. Ericsson M., Larsson V. E&MJ’s Annual Survey of Global Mining Investment. Engineering & Mining Journal. 2013. Vol. 214, No. 1. pp. 28–33.
3. China Mineral Resources 2015. Beijing : Geological Publishing House, 2015. 46 p.
4. Mineral commodity summaries 2016. U.S. Geological Survey, 2016. Available at: https://minerals.usgs.gov/minerals/pubs/mcs/2016/mcs2016.pdf (accessed: 19.04.2018).
5. Kazakova N. A., Gendon A. L., Sokolova E. S. Competitive global development of mining and chemical companies. Cambridge Journal of Education and Science. 2016. Vol. 3, No. 1(15). pp. 187–192.
6. Tulupov A. S. Compensation for environmental damage in the economy of mining. Gornyi Zhurnal. 2017. No. 8. pp. 61–65. DOI: 10.17580/gzh.2017.08.11
7. Melnikov N. N., Busyrev V. М. Сost of deposit reserve and its dependence on natural factors. News of the Higher Institutions. Mining Journal. 2010. No. 4. pp. 10–18.
8. Novoselov A. L., Novoselova I. Yu. Cost estimate of mineral deposits with regard for the public-andprivate partnership. Ecology of Urban Areas. 2017. No. 2. pp. 77–81.
9. Ampilov Yu. P. Cost Estimate of Subsoil : Educational Aid. Moscow : Geoinformmark, 2011. 408 p.
10. Medvedeva O. E., Melekhin E. S. Cost estimation of mineral deposits. Imushchestvennye otnosheniya v Rossiiskoi Federatsii. 2005. No. 3(42). pp. 61–68.
11. Melekhin E. S., Afonina I. A. Problems of geological-economic and cost assessment in subsoil use. Microeconomics. 2018. No. 1. pp. 28–30.
12. Piltan M., Mehmanchi E., Ghaderi S. F. Proposing a decision-making model using analytical hierarchy process and fuzzy expert system for prioritizing industries in installation of combined heat and power systems. Expert Systems with Applications. 2012. Vol. 39, Iss. 1. pp. 1124–1133.
13. Novoselov A. L., Medvedeva O. E., Novoselova I. Yu. Economy, Organization and Management in Subsoil Use : Textbook and Practical Course. Moscow : Yurait, 2016. 625 p.
14. Zadeh L. A. Fuzzy Sets. Information and Control. 1965. Vol. 8, Iss. 3. pp. 338–353.
15. Kiseleva I. A., Karmanov M. V., Korotkov A. V., Kuznetsov V. I., Gasparian M. S. Risk management in business: concept, types, evaluation criteria. Espacios. 2018. Vol. 39, No. 27. pp. 18–20.
16. Ptuskin A. S. Fuzzy Models and Methods in Management : Educational Aid. Moscow : Izdatelstvo MGTU im. N. E. Baumana, 2008. 216 p.
17. Myaskov A. V., Ilyin A. S., Popov S. M. Economical aspects of adaptation of open pit mine performance to fluctuations in the raw materials markets. Gornyi Zhurnal. 2017. No. 2. pp. 51–56. DOI: 10.17580/gzh.2017.02.09