CROSS-FACTOR MODELING OF ESG RISKS

Abstract

The purpose of this study is to develop a cross-factor model for assessing ESG risks in the Republic of Kazakhstan.The research methodology involves using cross-factor modeling with standard multivariate statistical methods, including multiple regression analysis, to assess ESG risks in Kazakhstan from 2010 to 2020. The methodology incorporates environmental, economic, and social components into a model, calculating their respective sums and determining the integral ESG indicator (I) through normalization and weighted averaging. The final stage involves conducting regression analysis to determine the coefficients for each component, ensuring that the model accurately reflects the impact of these factors on the region's economic security.The orginality / value of the research: This model aims to evaluate the mutual and combined effects of various ESG factors on integral indicators, with the goal of understanding how these factors can lead to reputational or financial damage to companies. By defining and measuring three structural components— environmental, economic, and social—the study constructs a set of indicators for each component and employs multiple regression analysis to build a comprehensive model and determine the coefficients for an integral ESG indicator.Findings. To assess ESG risks in Kazakhstan, cross-factor analysis methods are recommended, enabling the determination and justification of each factor's numerical contribution to the integral indicator. Recent dynamics in implementing ESG principles in Kazakhstan's corporate practice are uneven, influenced by factors such as company publicity and sector-specific ESG risks. Regulatory changes, including updates to the Environmental and Entrepreneurial Codes and ESG reporting requirements, create incentives for corporations to adopt ESG principles, necessitating further regulatory improvements and increased business community awareness.