Methodological Aspects in Evaluating the Likelihood of Unreliable Non-Financial Reporting by Coal Companies

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel quantitative model for evaluating NFR reliability, combining content analysis and industry benchmarks, which can be extended to other sectors.

🏢実務担当者:Useful for audit firms and ESG analysts to systematically assess the risk of unreliable disclosures in coal companies.

🏛政策担当者:Highlights the need for centralized ESG verification systems, supporting regulatory moves toward mandatory assurance.

The relevance of this research arises from the expanding influence of non-financial reporting (NFR) in achieving corporate transparency and cultivating confidence among investors, governments, and the public during the shift to sustainable development. The coal industry, facing significant environmental and social scrutiny, must address unreliable NFR indicators. This is crucial because the accuracy of ESG data directly influences companies’ reputations, investment opportunities, and regulatory standing. Inaccurate non-financial data, seen in formal reports, hidden negative information, and greenwashing, misrepresents companies’ true performance, requiring the creation of scientifically valid methods for detection and initial evaluation. The study aims   to develop and test a model for assessing the risk of NFR unreliability (the R index), considering the specifics of the coal industry and based on a combination of qualitative and quantitative indicators. The methodology is based on integrating current international standards (GRI 2021, SASB, ISSB IFRS S1/S2) and Russian regulatory documents —  the Bank of Russia’s methodological recommendations (2023) and the RSPP Sustainable Development Guidelines. The authors used content analysis methods , comparative analysis, data normalization, and expert assessment of indicators. The proposed model includes both qualitative and quantitative indicators. The former reflects the completeness, structure, and consistency of the disclosures. The latter characterizes the deviation of actual ESG indicators from industry benchmarks. Data normalization within the range [0;1] scale and the use of expert weighting enabled the formation of an integrated unreliability risk index R, which allows companies to be classified according to the risk levels of their NFR unreliability statements. The model’s testing involved a sample of major coal companies and was enhanced by an examination of industry indicators derived from data provided by Rosstat, Rosprirodnadzor, and ESG reports. Despite adhering to GRI and ISSB standards, the study’s results varying reliability in non-financial disclosures. It also identified a gap between reported ESG transparency and the actual consistency of the data. This reinforces the need to centralize ESG reporting verification, establish internal ESG audits, and adopt digital disclosure, aligning with the broader move towards more robust ESG disclosure verification. The scientific innovation of this research stems from creating a thorough model to numerically evaluate the trustworthiness of non-financial disclosures. This model synthesizes content analysis, quantitative assessment of metrics, and expert assignment of weights to these metrics. The R model enables unreliability risk identification by contrasting disclosures with industry parameters, a feature absent in current methods. The study’s outcomes   hold practical importance for audit firms, analysts, regulators, and rating agencies. They provide a foundation for developing a national system to monitor the accuracy of non-financial disclosures in high-risk economic sectors.

• semanticscholar https://etl.fa.ru/jour/article/download/587/410first seen 2026-07-18 07:42:01