ANTI-CRISIS INDICATORS OF CORPORATE FINANCIAL STABILITY: INTEGRATION OF ESG FACTORS AND STRESS TESTING

Main Article Content

Oleksandr YATSENKO

Abstract

Introduction. The contemporary economic environment is characterized by an unprecedented level of turbulence, where the frequency and severity of crisis phenomena – including pandemics, geopolitical conflicts, inflationary shocks, and disruptions to global supply chains – are steadily increasing. Simultaneously, non-financial risks encompassed by the Environmental, Social, and Governance (ESG) concept are gaining critical importance. Traditional crisis indicators (liquidity ratios, leverage ratios, Altman Z-scores) are retrospective, linear, and fail to capture the cascading effects of simultaneous shocks. This paper addresses the fundamental problem of developing an early-warning indicator system that integrates ESG factors with stress-testing methodology.


Materials and Methods. The study employs theoretical generalization, comparative analysis of existing early-warning models, and conceptual modeling. Based on a critical review of recent literature (2021–2026) and regulatory documents from the European Supervisory Authorities, the limitations of conventional indicators are identified. A three-level indicator system is proposed: (1) basic financial ratios, (2) ESG-adjusted metrics, and (3) stress-testing indicators. Scenario analysis includes baseline, moderate stress, and acute stress scenarios, parameterized with shocks to energy prices, demand, carbon taxation, and social unrest. The methodological framework recognizes ESG factors not as a separate risk category but as drivers of traditional financial risks – credit, market, and operational risks – thereby ensuring consistency with existing regulatory frameworks.


Results. The proposed indicators include the Carbon Intensity of Capital Index (CICI – Scope 1+2 emissions divided by equity), the Social License Index (SLI – a composite measure incorporating employee turnover, labor disputes, occupational safety, and local community survey results), and the Cash Flow Stress Indicator (CFSI – the ratio of stressed operating cash flow to stressed debt service). The parameters of three stress-testing scenarios are systematized in Table 1, and the hierarchical structure of the indicator system is visualized in Figure 1. In an illustrative example of a hypothetical industrial firm, the traditional Altman Z-score (2.8 – grey zone) suggested conditional financial health. However, the integrated stress test under an acute scenario (demand decline of 25%, energy price increase of 70%, carbon tax of €150 per ton) revealed a pre-crisis state: CICI increased from 0.4 to 0.9 t CO₂/thousand UAH capital, SLI dropped from 0.65 to 0.28, and CFSI equaled 0.65 – falling into the yellow zone according to the decision rule in Figure 1, indicating the need for immediate preventive measures.


Discussion. Compared to existing models, our contribution lies in providing an operational, firm-level dashboard that combines all three ESG pillars with scenario-based stress testing. The proposed system aligns with regulatory guidelines from the European Supervisory Authorities (2026) and the methodological frameworks developed by Adenutsi (2025) and Zaernyuk (2026). The main limitation is the need for empirical calibration of thresholds across industries and jurisdictions.


Conclusion. The study concludes that traditional crisis indicators are insufficient for modern risk landscapes. The proposed three-level system – integrating CICI, SLI, G-factor, and CFSI under acute stress scenarios – enhances early warning sensitivity. The scenario parameters systematized in Table 1 and the decision rules visualized in Figure 1 provide a practical toolkit for financial managers, credit institutions, and regulators. Future research should focus on large-sample empirical validation, industry-specific thresholds, and software automation for ESG stress testing.

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References

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