Application of covariance analysis and adaptive forecasting methods to evaluate circular economy strategies of metallurgical corporations

Subject. The processes of transformation of management models of metallurgical enterprises in the context of global climate regulation and digitalization. Objectives. Development and testing of integrated information and analytical tools for assessing the impact of circular economy strategies on the financial and environmental performance of corporations. Methods. The research is based on a complementary approach synthesizing methods of mathematical statistics and system analysis. Covariance analysis using fictitious variables is applied to quantify the impact of technological factors on profitability. The adaptive Brown double exponential smoothing model was used to predict the dynamics of production indicators and a life cycle assessment model in a specialized software environment for verifying environmental risks. Results. It has been established that the complete processing of by-products of metallurgical conversion is a statistically significant driver of net profit growth. A steady trend of increasing production volumes has been identified, which, under an inertial scenario, leads to a critical increase in ecotoxicity. The potential of the secondary raw materials market has been calculated and a strategic development matrix has been formed, allowing management to determine the optimal trajectories of technological modernization. Conclusions. The integration of mathematical modeling and environmental assessment makes it possible to justify the economic feasibility of moving from a linear model to a closed cycle, transforming man-made waste from liabilities into liquid assets and ensuring the financial stability of the business.