Estimation of initial financial and economic parameters of an investment project for mining companies; Оценка исходных финансово-экономических параметров инвестиционного проекта для горнодобывающих компаний

This study is devoted to the development of methodological foundations for improving the accuracy of estimation of initial financial and economic parameters of the investment project for mining companies and for decision-making under non-stochastic uncertainty. The purpose of the work is to apply the theory of subjective probabilities and the theory of fuzzy sets for formalization and analysis of financial and economic indicators, which is especially relevant in the context of high risks and multivariate initial data inherent in the mining industry. Uncertainty, being an integral part of any economic activity, creates risks of ineffective management, which can lead to failure to achieve the goals and objectives. In the framework of this study, the concept of risk of economic inefficiency of an investment project for mining companies is considered as a peculiarity of perception of uncertainty and conflict by stakeholders. This perception is caused by the possibility of non-compliance of actual economic results with the target or maximum permissible level, which emphasizes the need for in-depth risk assessment at the stage of investment decision-making. Special attention is paid to the application of fuzzy-multiple methodology for the assessment of financial and economic parameters. The authors of the study rely on the ideas of rough sets theory and offer a new method – the method of reference intervals, which allows building fuzzy estimates of initial financial and economic parameters of the analyzed investment project. This method takes into account the uncertainty and variability of initial data, which allows for more accurate modeling of risks and potential of investment decisions. Thus, this study offers a methodological basis for the application of the theory of subjective probabilities and fuzzy sets in order to improve the accuracy of estimates used in making investment decisions for mining companies. The application of the proposed approach helps to minimize the risks associated with data uncertainty and improves the quality of investment project planning in the mining sector, which in turn can lead to an increase in the overall economic efficiency of companies. © 2025 Elsevier B.V., All rights reserved.