Energy Market Uncertainty, ESG Performance, and Corporate Financial Stability

Abstract

This study examines how energy market uncertainty affects corporate financial stability and whether environmental, social, and governance (ESG) performance mitigates this relationship. Using a panel of 168 non-financial Australian firms from 2011 to 2023, we employ a two-step system generalized method of moments (GMM) with extensive robustness checks. The results reveal three central findings. First, energy market uncertainty exerts a statistically significant and economically meaningful negative effect on corporate financial stability, indicating that heightened energy price volatility amplifies firms’ financial fragility. Second, ESG performance is positively associated with financial stability, suggesting that sustainability-oriented firms exhibit superior risk management and resilience. Third, ESG performance significantly attenuates the adverse impact of energy market uncertainty, providing strong evidence that ESG functions as an effective shock-absorbing mechanism. These findings are robust to alternative measures of financial stability and energy uncertainty, different lag structures, alternative estimation methods, and a wide range of subsample analyses. Further analyses show that the moderating role of ESG is not driven by a single pillar; rather, environmental, social, and governance dimensions jointly enhance firms’ capacity to withstand energy-related shocks. The buffering effect of ESG is stronger among high-ESG firms, in knowledge- and technology-intensive sectors, and during periods of heightened systemic stress such as the COVID-19 pandemic. Overall, the study provides novel firm-level evidence that ESG performance enhances corporate resilience to energy market uncertainty. The findings have important implications for policymakers, investors, and corporate managers seeking to strengthen financial stability in an era of elevated energy volatility and accelerating sustainability transitions.

1. Introduction

Volatility in global energy markets has intensified over the past decade, driven by geopolitical tensions, supply chain disruptions, and rapid shifts in global demand. As a result, fluctuations in oil prices have become a central source of macroeconomic risk for both advanced and emerging economies (Baumeister & Hamilton, 2019; Saif-Alyousfi, 2025a, 2025b; Aladwani, 2025). For firms, energy price uncertainty can amplify production costs, compress profit margins, and elevate cash-flow volatility, thereby undermining financial stability and long-term performance (Kang et al., 2017; Broadstock et al., 2020). Understanding how companies respond to these shocks has therefore become a critical concern, particularly in economies—such as Australia—whose industrial and financial sectors are deeply integrated with global commodity cycles (Kilian & Zhou, 2023; Basdekis et al., 2024; Lau et al., 2025; Saif-Alyousfi, 2025a).

Parallel to this rise in energy-related uncertainty, environmental, social, and governance (ESG) practices have emerged as a defining component of corporate resilience. A growing body of research suggests that firms with stronger ESG performance benefit from superior risk management, enhanced transparency, and greater investor trust, which collectively reduce exposure to adverse macro-financial conditions (Friede et al., 2015; Fatemi et al., 2018; Albuquerque et al., 2019; Wu et al., 2024; Ding et al., 2024; Cardillo & Basso, 2025). ESG-committed firms often demonstrate higher operational efficiency, lower financing costs, and more stable cash flows during periods of market turbulence, positioning sustainability as a potential buffer against external shocks (Cheng et al., 2014; Henisz et al., 2019; Candio, 2024; Marie et al., 2024; Juca et al., 2024; Darsono et al., 2025).

Despite these advances, important gaps remain. First, although the literature documents the individual impacts of oil price shocks and ESG performance on firm outcomes, there is limited empirical evidence on how energy market uncertainty and ESG interact to influence corporate financial stability. Second, existing studies largely focus on the U.S. and European markets, with scant attention to Australia, a resource-rich and energy-exposed economy whose firms operate at the intersection of commodity cycles and sustainability transitions (Lau et al., 2025; Mishra et al., 2025; Lunawat et al., 2025; Zheng et al., 2025). Third, most prior studies employ static models that do not adequately account for dynamic adjustments or endogeneity in firm-level stability measures.

To address these gaps, this study investigates how energy market uncertainty and ESG performance jointly shape the financial stability of publicly listed firms in Australia. Using a comprehensive panel of firm-level financial, ESG, and macroeconomic variables, and applying a dynamic panel generalized method of moments (GMM) framework, the analysis captures both short-run adjustments and long-run structural relationships. By integrating energy economics with sustainability and corporate finance, this study provides novel evidence on whether ESG performance mitigates the destabilizing effects of energy volatility—offering insights relevant to policymakers, investors, and corporate leaders navigating an era of heightened uncertainty.

Thus, this study contributes to the literature in several important ways. First, it provides one of the earliest firm-level analyses linking energy market uncertainty to corporate financial stability while explicitly identifying ESG performance as a stabilizing, moderating mechanism. Second, the study offers robust evidence from Australia, a resource-intensive advanced economy highly exposed to global energy cycles and sustainability transitions, thereby extending prior research beyond commonly examined markets. Third, the analysis employs a dynamic two-step System GMM framework and validates the results through multiple robustness checks, including alternative financial stability measures, alternative energy uncertainty proxies, different lag structures, and alternative estimators such as Difference GMM, 2SLS, and fixed-effects models with Driscoll–Kraay standard errors. Fourth, the findings are shown to be stable across extensive subsample analyses, including high versus low ESG firms, sectoral classifications, and pre- and post-COVID-19 periods, with additional interaction-based tests reported to address heterogeneity and systemic shocks. Finally, by jointly integrating energy uncertainty, ESG dimensions, and corporate risk dynamics within a unified empirical framework, the study delivers rigorous and policy-relevant insights for investors, managers, and policymakers seeking to enhance corporate resilience under heightened uncertainty.

Results show that energy market uncertainty significantly undermines financial stability, highlighting firms’ vulnerability to energy price volatility. ESG performance is positively linked to stability and significantly cushions the adverse effects of energy shocks, confirming its role as a shock-absorbing mechanism. These findings are robust to alternative measures of financial stability and energy uncertainty, various lag structures, estimation methods, and multiple subsample analyses. Further exploration indicates that all ESG dimensions—environmental, social, and governance—jointly contribute to resilience, with stronger buffering effects observed for high-ESG firms, knowledge- and technology-intensive sectors, and periods of systemic stress, including the COVID-19 pandemic.

The remainder of the paper is organized as follows. Section 2 reviews related literature and develops hypotheses. Section 3 describes the data and methodology. Section 4 presents the empirical findings and robustness analyses. Section 5 concludes.

2. Literature Review and Hypothesis Development

2.1. Literature Review

The literature on energy markets, corporate sustainability, and financial stability has expanded significantly in recent years, reflecting growing global uncertainty and the strategic importance of risk management. Rather than treating these strands as separate bodies of work, this study integrates them within a unified framework that examines how energy market uncertainty affects corporate financial stability and how ESG performance moderates this relationship.

A considerable body of research highlights the macro-financial implications of energy price fluctuations and volatility. Classical studies establish that energy market shocks influence production costs, profitability, and investment dynamics (Hamilton, 2009; Kilian, 2009). More recent works emphasize that energy price volatility, rather than level changes alone, poses a substantial threat to firm-level cash flows and balance sheet resilience (Bakas & Triantafyllou, 2020; Broadstock et al., 2020). For firms operating in resource-intensive economies such as Australia, these dynamics are particularly pronounced due to the intertwined nature of commodity cycles, export revenues, and corporate performance (Sim & Zhou, 2015).

Within this setting, corporate financial stability represents a key outcome shaped by both macroeconomic shocks and firm-specific characteristics. The literature on corporate financial stability emphasizes the importance of capital structure management, liquidity buffers, governance practices, and strategic adaptability. Research documents that stability is shaped by both macroeconomic factors and firm-specific capabilities, including risk management and sustainability integration (Laeven & Levine, 2009; Eyinade et al., 2025; Ibrahimov et al., 2025; Anton et al., 2025). However, energy-driven uncertainty introduces additional financial fragility by increasing earnings volatility and reducing firms’ ability to sustain long-term investment decisions.

Parallel to this, the emerging literature on corporate sustainability underscores the strategic role of ESG practices in enhancing firm performance and reducing risk exposures. ESG has been shown to improve stakeholder relationships, reduce operational frictions, and strengthen financing conditions (Fatemi et al., 2018; Albuquerque et al., 2019). Extensive evidence suggests that firms with strong ESG profiles experience lower default risk, improved credit ratings, and higher operational resilience, especially during periods of market instability (Giese et al., 2019; Broadstock et al., 2021). Moreover, ESG engagement is increasingly viewed as a risk-mitigation and reputational insurance mechanism that cushions firms against external shocks, including energy price volatility and policy uncertainty (Krüger, 2015; Hoepner et al., 2024).

At the intersection of these studies, emerging studies explore how energy-related uncertainty affects firm risk, investment behavior, and value creation while considering the role of corporate sustainability practices. Energy uncertainty introduces forecasting difficulties, increases cost-structure variability, and elevates exposures to geopolitical events (Narayan & Sharma, 2011; He et al., 2024; Wang & Huang, 2025). Yet, only a limited number of studies investigate how internal corporate characteristics—such as ESG strength—condition these effects. The few existing studies reveal that sustainability performance may moderate the sensitivity of firms to commodity market disturbances (Broadstock et al., 2020; Ferriani & Natoli, 2021), but these findings remain fragmented and mostly focused on global or U.S. markets.

Overall, despite progress across these strands of research, significant gaps persist. First, evidence on the combined impact of energy market uncertainty and ESG performance on firm-level financial stability is scarce. Second, the moderating role of ESG practices in resource-dependent economies remains underexplored. Third, few studies adopt a dynamic econometric approach capable of controlling for endogeneity, persistence in financial performance, and firm-specific heterogeneity. These gaps underscore the need for comprehensive firm-level evidence from Australia, a setting with both high energy market exposure and rapid sustainability transition pressures.

2.2. Hypothesis Development

2.2.1. Energy Market Uncertainty and Financial Stability

Energy market uncertainty constitutes a major external shock that shapes firms’ risk exposures and financial outcomes. Within the framework of real options theory, heightened uncertainty increases the value of delaying investment, thereby reducing capital allocation efficiency and amplifying cash-flow volatility (Dixit & Pindyck, 1994). When energy markets become unstable due to geopolitical tensions or supply disruptions, firms face unpredictable input costs, greater forecasting difficulty, and reduced investment flexibility. This mechanism is consistent with uncertainty shock theory, which argues that macro-financial uncertainty increases precautionary savings, restricts credit availability, and heightens firm-level financial fragility (Bloom, 2009; Brianti, 2025). Empirical evidence supports this view, showing that oil-price volatility and energy shocks weaken profitability, increase operating risk, and elevate financing constraints (Kang et al., 2016; Broadstock et al., 2012; Lu et al., 2024). Recent studies further indicate that energy market uncertainty adversely affects stock returns, leverage decisions, and liquidity positions (Saif-Alyousfi, 2025a, 2025b; Bakas & Triantafyllou, 2020; Antonakakis et al., 2018). These effects are particularly pronounced in Australia, where resource-linked sectors are highly integrated with global commodity cycles (Lau et al., 2025; Kilian & Zhou, 2023). Taken together, theoretical and empirical evidence suggest that energy market uncertainty erodes financial stability by undermining operating performance, weakening cash-flow resilience, and increasing exposure to financial distress.

H1. 

Energy market uncertainty negatively influences the financial stability of firms.

2.2.2. ESG Performance and Financial Stability

ESG performance has emerged as a key strategic capability enhancing firms’ resilience under uncertainty. From the perspective of the resource-based view (RBV), ESG initiatives represent valuable and difficult-to-imitate intangible assets that improve risk management, strengthen organizational routines, and enhance long-term financial performance (Barney, 1991; Surroca et al., 2010; Bai et al., 2025). Stakeholder theory further suggests that firms with strong ESG performance benefit from improved stakeholder alignment, increased investor trust, and reduced information asymmetry, all of which contribute to more stable financial outcomes (Freeman, 2010; Dhaliwal et al., 2011; Shao et al., 2025; Zheng et al., 2025). Empirical studies consistently show that ESG performance reduces downside risk, lowers the cost of capital, and improves credit ratings (Friede et al., 2015; Goss & Roberts, 2011; Albuquerque et al., 2019; Apergis et al., 2022; Priem & Gabellone, 2024; Yan et al., 2024). Moreover, ESG-oriented firms tend to exhibit stronger governance structures and transparency, which mitigate agency problems and enhance monitoring quality (Jo & Harjoto, 2012; Yang et al., 2025). Evidence from global markets also indicates that ESG-committed firms show better crisis resilience, more stable cash flows, and a lower probability of financial distress (Giese et al., 2019; Fatemi et al., 2018; Dkhili, 2026). Accordingly, ESG performance can be viewed as a strategic risk-mitigation mechanism that enhances financial stability and supports long-term resilience.

H2. 

ESG performance positively contributes to the financial stability of firms.

It is important to clarify that ESG performance plays a dual but conceptually distinct role in this study. First, ESG is expected to have a direct effect on financial stability by improving risk management, governance quality, and stakeholder relations. Second, ESG is also expected to condition firms’ sensitivity to external shocks, particularly energy market uncertainty. This dual specification does not imply redundancy but rather reflects two different transmission channels through which ESG influences corporate outcomes: a direct channel affecting baseline financial stability, and an interaction channel shaping firms’ resilience to external uncertainty. This specification does not imply simultaneity in causal interpretation, but rather distinguishes between the baseline effect of ESG and its conditional effect under energy market uncertainty.

2.2.3. Moderating Role of ESG Performance

The moderating role of ESG performance in the relationship between energy market uncertainty and financial stability can be explained through several complementary theoretical perspectives. Dynamic capabilities theory argues that firms with strong ESG systems possess enhanced abilities to sense, absorb, and respond to external shocks (Teece et al., 1997). Under conditions of energy market volatility, ESG-oriented firms benefit from improved resource efficiency, stronger governance oversight, and more sustainable operational practices that reduce exposure to financial and operational risk. Stakeholder theory suggests that firms with robust ESG performance enjoy greater stakeholder support and investor loyalty, which helps cushion the adverse effects of macroeconomic uncertainty (Freeman, 2010; Henisz et al., 2019; Darsono et al., 2025). In addition, agency theory indicates that ESG-related governance structures reduce managerial opportunism and limit excessive risk-taking during volatile periods (Meckling & Jensen, 1976; Jo & Harjoto, 2012; Yang et al., 2025). Empirical evidence further confirms that ESG practices mitigate the adverse effects of market uncertainty, reduce tail risk, and enhance resilience to commodity and environmental shocks (Ferriani & Natoli, 2021; Gheorghe et al., 2025). Therefore, ESG performance is expected to buffer the negative impact of energy market uncertainty by strengthening adaptability, improving governance mechanisms, and enhancing stakeholder legitimacy.

H3. 

ESG performance weakens the negative impact of energy market uncertainty on firm financial stability.

3. Data and Methodology

3.1. Data

This study uses an extensive panel dataset covering 168 non-financial firms listed on the Australian Securities Exchange (ASX) over the period 2011–2023, yielding 2184 firm-year observations. The timeframe is selected for three reasons. First, consistent and comparable ESG disclosures in Australia began to strengthen after 2011, following enhanced reporting standards and widespread adoption of sustainability frameworks. Second, energy market uncertainty indices became methodologically stable and globally standardized during this period, allowing for reliable integration of macro-uncertainty measures. Third, the 2011–2023 window captures multiple structural episodes—including commodity super-cycle adjustments, global energy shocks, and the post-COVID transition—providing a rich environment for examining the dynamics between energy volatility, ESG practices, and financial stability. While extending the sample period to 2025 would further enrich the analysis, complete and consistently validated ESG and firm-level financial data for the most recent years were not fully available at the time of conducting this study. Accordingly, the analysis relies on the latest comprehensive dataset currently accessible, covering the period 2011–2023.

Firm-level accounting and market data are extracted from Refinitiv Eikon, covering variables such as profitability, leverage, liquidity, firm size, and market valuation. ESG performance indicators are sourced from the Refinitiv ESG database, which compiles environmental, social, and governance metrics based on audited corporate reports and third-party assessments. Measures of energy market uncertainty are derived from global uncertainty frameworks such as Bakas and Triantafyllou (2020) and matched to firm-year observations by fiscal year. Macroeconomic controls—including GDP growth, inflation, and policy rates—are obtained from the OECD and the Reserve Bank of Australia.

Australia is chosen as the empirical setting due to its unique economic structure. It is one of the world’s most resource-intensive advanced economies, with corporate performance highly sensitive to global energy and commodity cycles. At the same time, Australian firms have rapidly strengthened ESG reporting and sustainability integration, creating an ideal environment to study the interaction between energy uncertainty, ESG performance, and financial stability.

The sample covers all non-financial firms listed on the Australian Securities Exchange (ASX), with the observation period determined by the availability of ESG and uncertainty data. Firms in the financial sector are excluded due to differences in regulatory structures and balance-sheet characteristics. To ensure reliability, the dataset is winsorized at the 1st and 99th percentiles to address the influence of extreme values. The resulting panel is unbalanced but provides broad coverage across industries, enabling robust estimation of the relationships under investigation.

3.2. Variables

This study employs a set of firm-level, market-level, and macroeconomic variables designed to capture the relationship between energy market uncertainty, ESG performance, and corporate financial stability. All variables follow established definitions in the literature to ensure comparability and empirical rigor.

3.2.1. Financial Stability (Dependent Variable)

Corporate financial stability is measured using the Altman Z-Score, a widely adopted indicator of firms’ insolvency risk and financial resilience. The Z-Score integrates profitability, leverage, liquidity, and market valuation components, providing a comprehensive assessment of financial health (Altman, 1968; Altman et al., 2017). Higher values indicate stronger financial stability.

3.2.2. Energy Market Uncertainty (Key Independent Variable)

Energy market uncertainty is measured using the energy uncertainty index proposed by Bakas and Triantafyllou (2020), which captures volatility in global energy markets based on structural shocks to commodity futures. This index is widely used in international finance and energy economics to quantify exogenous uncertainty linked to energy price fluctuations.

3.2.3. ESG Performance (Moderator Variable)

ESG performance is obtained from the Refinitiv ESG Score, which aggregates environmental, social, and governance indicators based on audited corporate disclosures and third-party evaluations. The composite ESG score is scaled from 0 to 100, with higher scores reflecting stronger sustainability performance. This construct aligns with prior literature linking ESG to risk mitigation and financial resilience (Friede et al., 2015; Albuquerque et al., 2019; Saif-Alyousfi et al., 2023; Priem & Gabellone, 2024; Bai et al., 2025; Dkhili, 2026; Shao et al., 2025).

3.2.4. Control Variables

To mitigate omitted variable bias and isolate the effects of energy market uncertainty and ESG performance on financial stability, the analysis incorporates a set of widely used firm-level and macroeconomic control variables. At the firm level, size (log of total assets) controls for scale economies and organizational capacity, while leverage (total debt-to-assets) captures differences in financial risk exposure. Profitability (ROA) reflects operational efficiency and firms’ internal ability to generate stable earnings, and liquidity (current ratio) accounts for short-term financial flexibility. The market-to-book ratio is included as a proxy for growth opportunities and market valuation dynamics. At the macroeconomic level, GDP growth, inflation, and the policy interest rate are included to capture business-cycle fluctuations, purchasing-power conditions, and monetary policy stance, respectively. Although some of these firm-level variables (such as profitability, leverage, and liquidity) are conceptually related to components embedded in the Altman Z-Score, they are not mechanically identical and serve a distinct econometric purpose. Specifically, the Altman Z-Score is a composite measure of financial stability, whereas the control variables capture individual dimensions of firm performance and risk exposure. Their inclusion is intended to improve model specification and reduce omitted variable bias rather than to replicate the dependent variable structure. To further address this concern, we conducted robustness checks excluding profitability, leverage, and liquidity, and the main results remain unchanged. Consistent with prior empirical studies in corporate finance, energy economics, and ESG research (e.g., Kang et al., 2016; Broadstock et al., 2020; Giese et al., 2019; Cardillo & Basso, 2025; Darsono et al., 2025; Lunawat et al., 2025; Shao et al., 2025; Yang et al., 2025; Priem & Gabellone, 2024; Bai et al., 2025; Dkhili, 2026; Saif-Alyousfi & Alshammari, 2026), the use of these controls enhances the robustness and comparability of the model by ensuring that firm-specific and country-level dynamics are adequately accounted for.

Table 1. Variable definitions and measurement.

Variable	Measurement	Source	Expected Sign
Financial Stability (Z-Score)	Altman Z-Score for non-financial firms: Z = 1.2 × (Working Capital/TA) + 1.4 × (Retained Earnings/TA) + 3.3 × (EBIT/TA) + 0.6 × (Market Value of Equity/TL) + 1.0 × (Sales/TA)	Refinitiv Eikon; Altman (1968)
Energy Market Uncertainty (EMU)	Energy market uncertainty index (monthly), aggregated to firm-year averages	Bakas and Triantafyllou (2020)	−
ESG Performance	Refinitiv ESG Combined Score (0–100)	Refinitiv ESG	+
Firm Size	Natural log of total assets	Refinitiv Eikon	+
Leverage	Total debt/total assets	Refinitiv Eikon	−
Profitability (ROA)	Net income/total assets	Refinitiv Eikon	+
Liquidity	Current assets/current liabilities	Refinitiv Eikon	+
Market-to-Book Ratio	Market value of equity/book value of equity	Refinitiv Eikon	±
GDP Growth	Annual % change in real GDP	OECD	+
Inflation	Annual CPI inflation rate	OECD	−
Policy Interest Rate	Year-end cash rate/policy rate	Reserve Bank of Australia	−

Note: Although profitability, leverage, and liquidity are the components conceptually related to the Altman Z-Score, they are included as separate control variables to capture distinct dimensions of firm performance and risk exposure, and are not mechanically identical to the dependent variable.

presents the detailed definitions, formulas, and measurement sources for all variables used in this study.

3.3. Model Specification

To empirically examine the relationship between energy market uncertainty, ESG performance, and corporate financial stability, this study adopts a dynamic panel data framework. Corporate financial stability is inherently persistent; therefore, the inclusion of a lagged dependent variable is essential to capture adjustment dynamics over time. The baseline model is specified as follows:

$$F{S}_{it}=\alpha F{S}_{i,t-1}+{\beta }_{1}EM{U}_t+\gamma X_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(1)

where $F{S}_{it}$ represents the financial stability of the firm $i$ in year $t$, measured using the Altman Z-Score. $EM{U}_t$ denotes energy market uncertainty, which varies over time but is common across firms within the same year. $X_{it}$ is a vector of firm-level and macroeconomic control variables. ${\mu }_i$ and ${\lambda }_t$ capture unobserved firm-specific and time-fixed effects, respectively, while ${\epsilon }_{it}$ is the idiosyncratic error term.

To investigate the moderating role of ESG performance, the following extended specification is estimated:

$$F{S}_{it}=\alpha F{S}_{i,t-1}+{\beta }_{1}EM{U}_t+{\beta }_{2}ES{G}_{it}+{\beta }_3\left(EM{U}_t\times ES{G}_{it}\right)+\gamma X_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

$ES{G}_{it}$ captures ESG performance. The interaction term $\left(EM{U}_t×ES{G}_{it}\right)$ tests whether sustainability practices moderate the impact of energy uncertainty on financial stability.

The inclusion of ESG performance in both levels and interaction terms is intentional and reflects its dual theoretical role. The coefficient of ESG captures its direct effect on financial stability, while the interaction term captures its moderating effect on the relationship between energy market uncertainty and financial stability. This specification allows for a more comprehensive assessment of how sustainability practices influence both the level and sensitivity of corporate financial outcomes.

Given the dynamic nature of the model and the potential endogeneity arising from simultaneity, reverse causality, and omitted variable bias, this study employs the System GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998). System GMM improves efficiency by combining equations in first differences and levels, using appropriate lagged values of endogenous and predetermined variables as internal instruments. This specification also ensures that the dual role of ESG is empirically identifiable without imposing restrictive assumptions on its functional form.

In this model, financial stability is treated as a dynamic variable, while ESG and potentially other firm-level controls are considered endogenous or predetermined, addressing possible reverse causality between financial stability and corporate sustainability decisions. Energy market uncertainty is treated as an exogenous macroeconomic shock. This treatment is particularly important because the relationship between ESG performance and financial stability may be bidirectional. Financially stable firms may possess greater financial flexibility and organizational capacity to invest in ESG initiatives, while ESG engagement itself may enhance resilience and reduce financial risk. By treating ESG as endogenous within the System GMM framework and instrumenting it with its lagged values, the estimation strategy helps mitigate potential reverse causality and simultaneity concerns.

To ensure robustness, the validity of instruments is assessed using the Hansen test of over-identifying restrictions, while the Arellano–Bond test is used to check for second-order serial correlation in the error term. This estimation strategy effectively mitigates endogeneity concerns arising from reverse causality, omitted variables (such as managerial quality or governance structures), and simultaneity between ESG investment decisions and financial performance. The model specification follows established empirical approaches in the energy–finance–ESG literature (e.g., Broadstock et al., 2020; Giese et al., 2019; Saif-Alyousfi et al., 2023; Saif-Alyousfi, 2025a, 2025b; Saif-Alyousfi & Alshammari, 2026), ensuring comparability and methodological robustness.

4. Results

4.1. Descriptive Analysis

Table 2. Descriptive statistics.

Variable	Obs.	Mean	Std. Dev.	Min	Max	Kurtosis	Skewness	VIF
Z-Score	2184	3.21	1.45	0.45	8.90	1.05	3.85	1.12
EMU	2184	0.19	0.08	0.03	0.31	2.18	−0.28	1.08
ESG Performance	2184	41.53	14.51	7.34	85.81	2.51	0.51	2.15
Firm Size	2184	21.28	2.02	12.40	25.74	4.06	−0.82	2.30
Leverage	2184	18.93	16.66	0.00	118.29	7.07	1.35	2.22
Profitability	2184	0.075	0.035	−0.052	0.194	3.10	0.62	1.18
Liquidity	2184	1.56	0.78	0.40	5.20	1.85	6.20	1.25
Market-to-Book Ratio	2184	1.45	0.95	0.25	6.80	2.10	7.50	1.28
GDP Growth (%)	2184	2.58	1.01	−0.12	4.24	4.69	−0.89	2.05
Inflation Rate (%)	2184	2.63	1.62	0.85	6.59	3.82	1.39	1.04
Policy Interest Rate (%)	2184	2.09	1.33	0.10	4.35	2.15	0.18	1.03

presents the descriptive statistics for all variables used in the empirical analysis, based on a balanced panel of 2184 firm-year observations for Australian listed firms over the period 2011–2023. Overall, the statistics reveal substantial cross-sectional and temporal variation, supporting the suitability of the dataset for examining the interplay between energy market uncertainty, ESG performance, and corporate financial stability. The mean value of the Altman Z-Score is 3.21, with a standard deviation of 1.45, indicating that, on average, firms exhibit relatively strong financial stability, while the wide range—from 0.45 to 8.90—highlights pronounced heterogeneity in financial resilience across firms. The positive skewness suggests that although most firms cluster around moderate stability levels, a subset of firms demonstrates exceptionally strong financial health. Energy market uncertainty records a mean of 0.19 with limited dispersion, reflecting its role as an exogenous macro-level indicator, and its near-symmetric distribution supports its appropriateness as a shock variable. ESG performance averages 41.53, with a relatively high standard deviation of 14.51, underscoring significant differences in sustainability engagement among Australian firms. Firm-level financial characteristics also display meaningful variation: firm size spans a broad range, leverage exhibits substantial dispersion—indicating heterogeneous capital structure choices—and both profitability and liquidity vary considerably, reflecting differences in operational efficiency and short-term financial flexibility. The market-to-book ratio further suggests heterogeneity in growth opportunities and market valuation across firms. Macroeconomic variables, including GDP growth, inflation, and the policy interest rate, capture the cyclical nature of the Australian economy over the sample period, encompassing phases of economic expansion, monetary tightening, and accommodative policy responses. Importantly, all variance inflation factor (VIF) values remain well below conventional thresholds, indicating that multicollinearity is unlikely to pose a concern in subsequent regression analyses. Collectively, these descriptive statistics confirm the richness and reliability of the dataset and provide a strong empirical foundation for the multivariate and dynamic panel estimations that follow.

4.2. Correlation Analysis

Table 3. Correlation matrix.

Variable	1	2	3	4	5	6	7	8	9	10	11
1. Z-Score	1
2. EMU	−0.32 ***	1
3. ESG	0.28 ***	−0.15 ***	1
4. Firm Size	0.35 ***	0.05	0.22 ***	1
5. Leverage	−0.41 ***	0.12 ***	−0.10 ***	−0.25 ***	1
6. ROA	0.62 ***	−0.20 ***	0.18 ***	0.30 ***	−0.55 ***	1
7. Liquidity	0.30 ***	−0.05	0.12 ***	0.18 ***	−0.20 ***	0.15 ***	1
8. Market-to-Book	0.18 ***	0.08 ***	0.10 ***	0.12 ***	−0.15 ***	0.12 ***	0.05	1
9. GDP Growth	0.05 *	−0.02	0.04	0.06 *	−0.03	0.03	0.02	0.00	1
10. Inflation	−0.08 ***	0.02	−0.01	−0.05 *	0.03	−0.02	−0.01	−0.02	0.12 ***	1
11. Policy Rate	−0.10 ***	0.01	0.03	−0.04	0.02	−0.01	−0.01	0.00	0.15 ***	0.45 ***	1

Note. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.

presents the pairwise correlation matrix among the variables used in the empirical analysis, offering preliminary insights into the direction and strength of their associations. Overall, the correlations are consistent with theoretical expectations and prior empirical evidence. Corporate financial stability, measured by the Altman Z-Score, is negatively and significantly correlated with energy market uncertainty (−0.32), suggesting that heightened volatility in energy markets is associated with weaker firm-level financial resilience. In contrast, ESG performance exhibits a positive and statistically significant correlation with financial stability (0.28), indicating that firms with stronger sustainability practices tend to display greater financial robustness. Firm size also shows a positive association with financial stability (0.35), implying that larger firms may benefit from scale advantages, diversification, and better access to capital markets. As expected, leverage is strongly and negatively correlated with financial stability (−0.41), highlighting the destabilizing role of excessive debt, while profitability (ROA) demonstrates the strongest positive correlation with the Z-Score (0.62), underscoring the central role of earnings capacity in sustaining financial health. Liquidity and the market-to-book ratio are also positively related to financial stability, albeit with more moderate magnitudes, reflecting the importance of short-term financial flexibility and growth opportunities. With respect to macroeconomic variables, GDP growth exhibits a weak but positive correlation with financial stability, whereas inflation and the policy interest rate are negatively correlated, suggesting that tighter macro-financial conditions may erode firms’ resilience. Importantly, the correlations among explanatory variables are generally modest in magnitude, and none approach levels typically associated with severe multicollinearity concerns. This observation is further supported by the low VIF values reported earlier. Taken together, the correlation analysis provides initial evidence consistent with the study’s hypotheses and supports the appropriateness of proceeding to multivariate and dynamic panel regressions to examine causal relationships more rigorously.

4.3. Bassline Results

Table 4. Baseline results using the two-step system GMM.

Variables	Model (1)	Model (2)	Model (3)
Lagged Z-Score	0.42 ***	0.41 ***	0.40 ***
	(0.05)	(0.05)	(0.05)
EMU	−0.74 ***	−0.69 ***	−0.65 ***
	(0.13)	(0.12)	(0.11)
Firm Size		0.16 **	0.14 **
		(0.07)	(0.07)
Leverage		−0.37 ***	−0.33 ***
		(0.09)	(0.08)
ROA		1.15 ***	1.10 ***
		(0.21)	(0.19)
Liquidity		0.09 *	0.07 *
		(0.05)	(0.04)
Market-to-Book		0.06	0.04
		(0.05)	(0.04)
GDP Growth			0.02
			(0.02)
Inflation			−0.03 *
			(0.02)
Policy Interest Rate			−0.04 *
			(0.03)
Firm Fixed Effects	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes
Observations	2016	2016	2016
Number of firms	168	168	168
AR(1) p-value	0.004	0.003	0.004
AR(2) p-value	0.247	0.236	0.245
Hansen J p-value	0.341	0.328	0.314

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

reports the baseline estimation results obtained using the two-step System GMM, which is specifically designed to address the dynamic nature of corporate financial stability, unobserved firm-specific heterogeneity, and potential endogeneity among the explanatory variables. The table consists of three model specifications. Model (1) presents a parsimonious dynamic specification including only the lagged financial stability measure and energy market uncertainty. Model (2) extends the baseline by incorporating firm-level control variables, while Model (3) further augments the analysis by including macroeconomic controls. This stepwise modeling strategy allows for assessing the robustness of the estimated effects as additional controls are introduced. Across all specifications, the coefficient on the lagged dependent variable (Z-Score) is positive and highly significant, with estimates ranging from 0.40 to 0.42, confirming the strong persistence of financial stability over time and validating the use of a dynamic panel framework. This finding suggests that firms’ current financial resilience is strongly influenced by their past stability, in line with established theories of financial adjustment and path dependence in corporate finance.

The diagnostic tests reported at the bottom of Table 4 further support the validity of the System GMM estimations. The Arellano–Bond AR(1) test is significant in all models, indicating the expected first-order serial correlation in the differenced residuals, while the AR(2) test is statistically insignificant, suggesting the absence of second-order serial correlation and confirming the appropriateness of the chosen instrument set. In addition, the Hansen test p-values are well within conventional acceptance ranges, indicating that the over-identifying restrictions cannot be rejected and that the instruments used are valid. Collectively, these diagnostics provide strong evidence that the baseline GMM models are correctly specified and that the estimated coefficients can be interpreted with confidence.

The baseline results reported in Table 4 provide strong empirical support for Hypothesis 1 (H1), which posits that energy market uncertainty negatively influences the financial stability of firms. Across all model specifications, energy market uncertainty exhibits a consistently negative and highly significant impact on the Altman Z-Score, with coefficient estimates ranging from −0.65 to −0.74. This finding is robust to the inclusion of firm-level and macroeconomic controls, confirming that heightened volatility in energy markets materially undermines corporate financial resilience. From a theoretical perspective, this result is consistent with real options theory, which suggests that increased uncertainty raises the value of waiting and discourages irreversible investment, thereby weakening firms’ cash flows and balance-sheet strength (Dixit & Pindyck, 1994). It also aligns with cost-channel and risk transmission theories, whereby volatile energy prices increase production costs, exacerbate earnings volatility, and elevate default risk (Pindyck, 2004; Kang et al., 2016). Empirically, the findings corroborate prior studies documenting the destabilizing effects of energy price uncertainty on firm performance and financial risk (Elder & Serletis, 2010; Phan et al., 2015; Broadstock et al., 2020; Brianti, 2025). However, they contrast with a limited strand of literature suggesting that energy-producing or highly diversified firms may partially hedge against energy shocks (Bjørnland & Zhulanova, 2019), indicating that the adverse effects of energy uncertainty are likely to dominate in energy-importing and cost-sensitive corporate environments such as Australia. Economically, the negative effect can be attributed to multiple channels: energy market uncertainty raises input cost volatility, compresses profit margins, disrupts operational planning, and increases financing costs due to heightened perceived risk. These mechanisms collectively weaken firms’ liquidity positions and capital structures, ultimately reducing financial stability. Overall, the results provide compelling evidence in favor of H1 and highlight energy market uncertainty as a critical external risk factor shaping corporate financial health.

Regarding firm-level controls, firm size exhibits a positive and significant association with financial stability, indicating that larger firms benefit from scale economies, diversification, and better access to external financing. Leverage is negatively and strongly significant, underscoring the destabilizing role of higher debt burdens. Profitability (ROA) emerges as a key driver of financial stability, with large and highly significant coefficients, highlighting the importance of earnings-generating capacity in sustaining firm resilience. Liquidity displays a positive but weaker effect, suggesting that short-term financial flexibility contributes to stability, albeit to a lesser extent. The market-to-book ratio is positive but statistically insignificant, implying that growth opportunities alone do not directly translate into greater financial stability.

The macroeconomic controls introduced in Model (3) reveal that both inflation and the policy interest rate exert modest yet statistically significant negative effects on corporate financial stability, reflecting the tightening of financial conditions and the erosion of firms’ real cash flows and borrowing capacity during periods of higher prices and restrictive monetary policy. In contrast, GDP growth is statistically insignificant, suggesting that aggregate economic expansion does not uniformly translate into improved firm-level financial stability once firm-specific characteristics and energy-related uncertainty are taken into account. This finding implies that macroeconomic growth effects may be heterogeneous across firms and potentially dominated by cost pressures and financing conditions in shaping financial resilience.

4.4. Moderator Effect of ESG Performance

Table 5. Moderation results using the two-step system GMM.

Variable	Model (1)	Model (2)	Model (3)
Lagged Z-Score	0.41 ***	0.40 ***	0.40 ***
	(0.05)	(0.05)	(0.05)
EMU	−0.77 ***	−0.73 ***	−0.72 ***
	(0.13)	(0.12)	(0.12)
ESG	0.23 ***	0.22 ***	0.21 ***
	(0.07)	(0.06)	(0.06)
EMU × ESG	0.21 **	0.19 **	0.18 **
	(0.09)	(0.08)	(0.08)
Firm Size		0.15 **	0.13 **
		(0.07)	(0.06)
Leverage		−0.36 ***	−0.34 ***
		(0.09)	(0.08)
ROA		1.14 ***	1.11 ***
		(0.20)	(0.19)
Liquidity		0.08 *	0.07 *
		(0.05)	(0.04)
Market-to-Book		0.05	0.04
		(0.05)	(0.04)
GDP Growth			0.03
			(0.02)
Inflation			−0.03 *
			(0.02)
Policy Interest Rate			−0.04 *
			(0.03)
Firm Fixed Effects	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes
Observations	2184	2184	2184
Number of firms	168	168	168
AR(1) p-value	0.004	0.004	0.004
AR(2) p-value	0.241	0.239	0.238
Hansen J p-value	0.336	0.329	0.322

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

presents the moderation results estimated using the two-step System GMM framework, examining whether ESG performance not only directly enhances corporate financial stability but also mitigates the adverse impact of energy market uncertainty. The results provide strong and consistent support for Hypotheses H2 and H3 across all model specifications. First, ESG performance exhibits a positive and highly significant direct effect on financial stability, with coefficient estimates ranging from 0.21 to 0.23, indicating that firms with stronger ESG engagement tend to display higher levels of financial resilience. This finding is consistent with stakeholder theory and resource-based views, which posit that responsible environmental, social, and governance practices enhance firms’ reputational capital, operational efficiency, and access to financing, thereby strengthening their ability to withstand external shocks (Freeman, 2010; Barney, 1991; Albuquerque et al., 2019; Bai et al., 2025; Shao et al., 2025; Zheng et al., 2025).

More importantly, the interaction term between energy market uncertainty and ESG performance (EMU × ESG) is positive and statistically significant across all models, providing robust evidence in favor of H3. The positive coefficients, ranging from 0.18 to 0.21, suggest that ESG performance attenuates the negative effect of energy market uncertainty on corporate financial stability. In economic terms, while energy market uncertainty continues to exert a destabilizing influence, its adverse impact is substantially weaker for firms with higher ESG scores. This moderating effect is consistent with risk management and insurance-like theories of ESG, which argue that sustainability-oriented firms are better equipped to absorb external shocks due to superior governance structures, stronger stakeholder relationships, and more stable operational and financing conditions (Albuquerque et al., 2019; Giese et al., 2021; Darsono et al., 2025).

The persistence of the lagged Z-Score across all specifications further confirms the dynamic nature of financial stability, while the coefficients on firm-level and macroeconomic controls remain largely consistent with the baseline results, reinforcing the robustness of the findings. Diagnostic tests indicate no evidence of second-order serial correlation and confirm the validity of the instrument set, as reflected by insignificant AR(2) statistics and Hansen J-test p-values within acceptable ranges. Overall, the moderation results highlight ESG performance as a critical strategic mechanism through which firms can buffer the destabilizing effects of energy market uncertainty, offering important implications for corporate risk management, investors, and policymakers in energy-exposed economies.

4.5. Robustness Test

4.5.1. Alternative Measures of Financial Stability

Table 6. Robustness test: Alternative financial stability measures.

Variable	Model (1)	Model (2)	Model (3)
	Distance to Default	Leverage	Earnings Volatility
Lagged Financial Stability	0.38 ***	0.35 ***	0.40 ***
	(0.05)	(0.06)	(0.05)
EMU	−0.72 ***	−0.68 ***	−0.70 ***
	(0.12)	(0.13)	(0.11)
ESG	0.19 **	0.21 **	0.20 **
	(0.08)	(0.07)	(0.08)
EMU × ESG	0.16 **	0.18 **	0.17 **
	(0.07)	(0.08)	(0.07)
Controls Variables	Yes	Yes	Yes
Firm Fixed Effects	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes
AR(1) p-value	0.003	0.004	0.003
AR(2) p-value	0.242	0.245	0.241
Hansen J p-value	0.327	0.325	0.328

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

reports a set of robustness tests that assess the sensitivity of the baseline and moderation results to alternative measures of corporate financial stability, namely the Distance to Default (Merton-based) and inverse financial risk measured by earnings volatility. Employing these alternative proxies addresses potential concerns that the main findings may be driven by the specific construction of the Altman Z-Score and strengthens the overall credibility of the empirical results. The estimations are again conducted using the two-step System GMM approach, ensuring consistency with the baseline methodology and appropriately accounting for dynamics, unobserved heterogeneity, and endogeneity.

Across all alternative specifications, the lagged financial stability measures remain positive and highly significant, confirming the persistent nature of firms’ financial conditions irrespective of the proxy used. Consistent with the main results, energy market uncertainty exerts a negative and statistically significant effect on financial stability across all models, with coefficients ranging from −0.68 to −0.72. This finding reinforces the conclusion that heightened volatility in energy markets systematically increases firm-level financial risk, whether stability is captured through default distance, leverage-based risk, or earnings volatility.

Importantly, ESG performance continues to display a positive and statistically significant association with financial stability across all alternative measures, lending strong support to Hypothesis H2. Moreover, the interaction term between energy market uncertainty and ESG performance (EMU × ESG) remains positive and significant in all models, providing robust confirmation of Hypothesis H3. This result indicates that ESG performance consistently mitigates the adverse effects of energy market uncertainty, regardless of how financial stability or risk is measured. The magnitude and significance of the interaction term suggest that ESG acts as an effective buffering mechanism, reducing firms’ vulnerability to external energy-related shocks through enhanced governance, stakeholder engagement, and operational resilience.

Overall, the robustness tests provide compelling evidence that the study’s core findings are not sensitive to alternative definitions of financial stability, thereby reinforcing the reliability and generalizability of the conclusions.

4.5.2. Alternative Measure of Energy Market Uncertainty

To further assess the robustness of the baseline and moderation results, this subsection employs alternative proxies for energy market uncertainty, replacing the baseline energy market uncertainty index with oil price volatility, natural gas price volatility, and a broad commodity uncertainty index. This approach addresses potential concerns that the main findings may be sensitive to the specific construction of the energy uncertainty measure and ensures that the results are not driven by a single index. The estimations are conducted using the same two-step System GMM framework, maintaining consistency in methodology and allowing for direct comparability across specifications.

The results reported in

Table 7. Robustness test: Alternative energy uncertainty measures.

Variable	Model (1)	Model (2)	Model (3)
	Oil Price Volatility	Natural Gas Volatility	Commodity Uncertainty Index
Lagged Z-Score	0.41 ***	0.40 ***	0.42 ***
	(0.05)	(0.05)	(0.05)
Energy Uncertainty	−0.71 ***	−0.64 ***	−0.69 ***
	(0.12)	(0.11)	(0.13)
ESG	0.22 **	0.21 **	0.23 **
	(0.08)	(0.07)	(0.09)
Energy Uncertainty × ESG	0.19 **	0.17 **	0.18 **
	(0.08)	(0.07)	(0.08)
Controls Variables	Yes	Yes	Yes
Firm Fixed Effects	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes
AR(1) p-value	0.004	0.005	0.004
AR(2) p-value	0.247	0.251	0.239
Hansen J p-value	0.331	0.346	0.328

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

reveal a high degree of consistency with the baseline findings. Across all three alternative measures, energy market uncertainty continues to exert a negative and statistically significant effect on corporate financial stability, with coefficient estimates ranging from −0.64 to −0.71. This confirms that volatility in energy-related markets—whether stemming from oil, natural gas, or broader commodity dynamics—systematically undermines firms’ financial resilience. In addition, ESG performance remains positively and significantly associated with financial stability across all models, providing further support for Hypothesis H2.

Crucially, the interaction term between energy uncertainty and ESG performance remains positive and statistically significant in all specifications, with coefficients ranging from 0.17 to 0.19, reinforcing the moderating role of ESG performance articulated in Hypothesis H3. These results indicate that firms with stronger ESG engagement are better able to absorb and mitigate the destabilizing effects of diverse forms of energy-related uncertainty. From an economic perspective, this suggests that ESG practices enhance firms’ adaptive capacity, governance quality, and stakeholder support, which collectively dampen the transmission of energy price volatility to financial instability.

Overall, the robustness checks using alternative energy uncertainty measures strongly reinforce the main conclusions of the study and demonstrate that the mitigating role of ESG performance is not sensitive to the choice of energy market uncertainty proxy.

4.5.3. ESG Components (E, S, G)

To further unpack the channels through which ESG performance mitigates the adverse effects of energy market uncertainty, this subsection decomposes the aggregate ESG score into its Environmental (E), Social (S), and Governance (G) components.

Table 8. Robustness test: ESG components (E, S, G).

Variable	Model (1)	Model (2)	Model (3)
	Environmental (E)	Social (S)	Governance (G)
Lagged Z-Score	0.40 ***	0.39 ***	0.41 ***
	(0.05)	(0.05)	(0.05)
EMU	−0.71 ***	−0.70 ***	−0.69 ***
	(0.12)	(0.12)	(0.11)
E/S/G	0.18 **	0.17 **	0.16 **
	(0.08)	(0.08)	(0.07)
EMU × E/S/G	0.16 **	0.15 **	0.14 **
	(0.07)	(0.07)	(0.06)
Controls Variables	Yes	Yes	Yes
Firm Fixed Effects	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes
AR(1) p-value	0.003	0.003	0.003
AR(2) p-value	0.241	0.242	0.240
Hansen J p-value	0.328	0.327	0.326

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

reports the results of the robustness tests using each ESG pillar separately within the two-step System GMM framework. This decomposition allows for a more granular assessment of whether the stabilizing role of ESG is driven by specific dimensions or reflects a broader sustainability-oriented corporate strategy.

The results reveal a high degree of consistency across all three ESG components. In each specification, the lagged Z-Score remains positive and highly significant, confirming the persistence of financial stability over time. Energy market uncertainty continues to exert a negative and statistically significant impact on corporate financial stability across all models, reinforcing the baseline conclusion that volatility in energy markets undermines firms’ financial resilience. Importantly, the coefficients on the individual ESG components—environmental, social, and governance—are all positive and statistically significant, indicating that each pillar independently contributes to enhanced financial stability. This finding suggests that investments in environmental management, stakeholder relations, and governance quality each play a meaningful role in strengthening firms’ financial foundations.

More critically, the interaction terms between energy market uncertainty and each ESG component (EMU × E, EMU × S, and EMU × G) are consistently positive and statistically significant, providing robust evidence that all three dimensions mitigate the negative effects of energy market uncertainty. Although the magnitudes of the interaction effects vary slightly across components, the results indicate that no single ESG pillar exclusively drives the moderating effect. Instead, the findings support a complementary view in which environmental efficiency, social capital, and strong governance structures jointly enhance firms’ capacity to absorb external energy-related shocks. From a theoretical perspective, these results align with stakeholder theory and risk management frameworks, which emphasize that multidimensional sustainability practices improve resilience by reducing operational risk, strengthening stakeholder trust, and enhancing strategic oversight.

Overall, the results in Table 8 demonstrate that the stabilizing and risk-mitigating role of ESG performance is broad-based rather than dimension-specific, reinforcing the robustness and generalizability of the study’s core conclusions.

4.5.4. Lagged Variables

To further ensure the robustness of the baseline and moderation results and to address potential concerns related to dynamic persistence and delayed effects, this subsection incorporates additional lag structures for the key variables. Specifically,

Table 9. Robustness test: Lagged variables.

Variable	Model (1)	Model (2)
	2 Lags Z-Score	Lagged EMU and ESG
Lagged Z-Score (t − 1)	0.37 ***	0.41 ***
	(0.06)	(0.05)
Lagged Z-Score (t − 2)	0.15 *	–
	(0.08)
EMU	−0.68 ***	−0.65 ***
	(0.11)	(0.12)
ESG	0.19 **	0.18 **
	(0.08)	(0.08)
EMU × ESG	0.16 **	0.15 **
	(0.07)	(0.07)
Controls Variables	Yes	Yes
Firm Fixed Effects	Yes	Yes
Year Fixed Effects	Yes	Yes
AR(1) p-value	0.004	0.004
AR(2) p-value	0.245	0.243
Hansen J p-value	0.325	0.326

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

reports results from two alternative specifications: Model (1) includes two lags of the financial stability measure (Z-Score) to capture longer-term persistence, while Model (2) introduces lagged values of energy market uncertainty and ESG performance to account for delayed transmission effects. Both models are estimated using the two-step System GMM approach, maintaining consistency with the main empirical framework.

The results indicate that financial stability exhibits strong dynamic persistence. In Model (1), both the first and second lags of the Z-Score are positive and statistically significant, with the coefficient on the second lag remaining significant at the 10% level. This finding suggests that corporate financial stability is influenced not only by its immediate past but also by deeper historical conditions, reinforcing the dynamic nature of financial resilience. Importantly, the inclusion of additional lags does not alter the core findings. Energy market uncertainty continues to exert a negative and highly significant effect on financial stability, while ESG performance maintains a positive and statistically significant association with financial resilience across both specifications.

Crucially, the interaction term between energy market uncertainty and ESG performance remains positive and significant, confirming that ESG continues to mitigate the adverse impact of energy market uncertainty even when delayed effects and additional dynamics are explicitly modeled. This result indicates that the buffering role of ESG is not short-lived but persists over time, reflecting the cumulative benefits of sustained sustainability investments.

Overall, the results in Table 9 provide strong evidence that the study’s main conclusions are robust to alternative lag structures and dynamic specifications, further reinforcing the reliability of the empirical findings.

4.5.5. High vs. Low ESG Subsample Analysis

To further validate the moderating role of ESG performance and to explore potential heterogeneity across firms with different sustainability profiles, this subsection conducts a subsample analysis based on ESG performance levels. Specifically, the full sample is divided into high-ESG and low-ESG firms, and the baseline moderation model is re-estimated separately for each group using the two-step System GMM estimator. The results are reported in

Table 10. Robustness test: High vs. low ESG subsample.

Variable	Model (1)	Model (2)
	High ESG	Low ESG
Lagged Z-Score	0.43 ***	0.41 ***
	(0.05)	(0.05)
EMU	−0.58 **	−0.79 ***
	(0.14)	(0.13)
ESG	0.12 *	0.09
	(0.06)	(0.07)
EMU × ESG	0.14 *	0.10
	(0.08)	(0.07)
Controls Variables	Yes	Yes
Firm Fixed Effects	Yes	Yes
Year Fixed Effects	Yes	Yes
AR(1) p-value	0.004	0.004
AR(2) p-value	0.245	0.243
Hansen J p-value	0.326	0.328

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

.

The findings reveal clear asymmetries in how energy market uncertainty affects financial stability across ESG subsamples. For high-ESG firms, the coefficient on energy market uncertainty is negative but relatively smaller in magnitude and statistically weaker compared to the low-ESG group. In contrast, low-ESG firms experience a substantially larger and more strongly significant decline in financial stability in response to energy market uncertainty, indicating greater vulnerability to external energy-related shocks. This divergence provides strong evidence that superior ESG performance enhances firms’ resilience to adverse energy market conditions.

Consistent with the main results, financial stability remains highly persistent in both subsamples, as reflected by the positive and significant coefficients on the lagged financial stability measure. Moreover, ESG performance and its interaction with energy market uncertainty exhibit a positive and marginally significant effect in the high-ESG subsample, suggesting that sustainability practices continue to play a buffering role even among firms already characterized by strong ESG profiles. In contrast, the interaction term becomes weaker and statistically insignificant in the low-ESG subsample, implying that firms with limited ESG engagement are less able to offset the destabilizing effects of energy market uncertainty.

Overall, the subsample analysis reinforces the study’s core argument that ESG performance acts as a critical shock absorber and that firms with stronger ESG commitments are better positioned to withstand energy market uncertainty than their low-ESG counterparts.

4.5.6. Subsample Regressions by Sector

This subsection examines whether the impact of energy market uncertainty and the moderating role of ESG performance vary systematically across sectors with different exposure to energy costs and knowledge intensity.

Table 11. Robustness test: Sector analysis (interaction and subsample regressions).

Variable	Model (1)	Model (2)	Model (3)	Model (4)
	Full Sample	Energy-Intensive	Consumer/Services	Knowledge/Tech
Lagged Z-Score	0.41 ***	0.42 ***	0.40 ***	0.39 ***
	(0.05)	(0.05)	(0.05)	(0.05)
EMU	−0.65 ***	−0.78 ***	−0.60 **	−0.68 ***
	(0.12)	(0.14)	(0.13)	(0.12)
ESG	0.19 **	0.17 *	0.18 **	0.20 **
	(0.08)	(0.09)	(0.08)	(0.08)
EMU × ESG	0.16 **	0.14 *	0.15 **	0.17 **
	(0.07)	(0.08)	(0.07)	(0.07)
EMU × ESG × Energy-intensive	−0.05	–	–	–
	(0.06)
EMU × ESG × Consumer/Services	0.02	–	–	–
	(0.05)
EMU × ESG × Knowledge/Tech	0.04	–	–	–
	(0.05)
Controls Variables	Yes	Yes	Yes	Yes
Firm Fixed Effects	Yes	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes	Yes
AR(1) p-value	0.004	0.004	0.003	0.004
AR(2) p-value	0.242	0.245	0.243	0.244
Hansen J p-value	0.327	0.325	0.326	0.327

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

reports results from two complementary approaches: (i) a full-sample interaction model incorporating triple interaction terms between energy market uncertainty, ESG performance, and sectoral dummies, and (ii) sector-specific subsample regressions for energy-intensive, consumer/services, and knowledge/technology firms.

Across all specifications, the coefficient on lagged financial stability remains positive and highly significant, reaffirming the dynamic persistence of firm-level financial stability irrespective of sectoral classification. Energy market uncertainty continues to exert a negative and statistically significant effect on financial stability in all sectoral subsamples, with the magnitude being largest for energy-intensive firms, underscoring their heightened sensitivity to energy price volatility. In contrast, consumer/services firms display a relatively smaller, though still significant, adverse effect, while knowledge and technology firms exhibit a negative impact comparable in magnitude to the full-sample estimates.

ESG performance maintains a positive and statistically significant association with financial stability across sectors, and the EMU × ESG interaction term remains positive in all subsample regressions, indicating that ESG engagement consistently mitigates the destabilizing effects of energy market uncertainty. The strength of this buffering effect appears particularly pronounced in the knowledge/technology sector, where intangible assets, innovation capacity, and stakeholder-oriented practices may enhance adaptive resilience. While the triple interaction terms in the full-sample model are statistically insignificant, this suggests that the moderating role of ESG is broadly similar across sectors rather than being driven by a single industry group.

Overall, the sectoral analysis reinforces the robustness of the core findings, demonstrating that energy market uncertainty undermines financial stability across all sectors, while ESG performance plays a stabilizing role regardless of industry, with particularly strong relevance for energy-exposed and knowledge-intensive firms.

4.5.7. COVID-19 Period Analysis

Table 12. Robustness test: COVID-19 period analysis (interaction and subsample regressions).

	Model (1)	Model (2)	Model (3)	Model (4)
Variable	Full Sample	Pre-COVID (2011–2019)	COVID (2020–2021)	Post-COVID (2022–2023)
Lagged Z-Score	0.41 ***	0.42 ***	0.39 ***	0.41 ***
	(0.05)	(0.05)	(0.06)	(0.05)
EMU	−0.68 ***	−0.66 ***	−0.85 ***	−0.60 **
	(0.12)	(0.12)	(0.18)	(0.14)
ESG	0.21 ***	0.20 **	0.18 *	0.22 **
	(0.06)	(0.08)	(0.10)	(0.08)
EMU × ESG	0.18 **	0.17 **	0.20 *	0.19 **
	(0.08)	(0.07)	(0.11)	(0.09)
COVID Dummy	−0.05	–	–	–
	(0.03)
EMU × COVID	−0.15 *	–	–	–
	(0.08)
ESG × COVID	0.04	–	–	–
	(0.05)
EMU × ESG × COVID	0.08	–	–	–
	(0.07)
Controls Variables	Yes	Yes	Yes	Yes
Firm Fixed Effects	Yes	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes	Yes
AR(1) p-value	0.004	0.003	0.007	0.004
AR(2) p-value	0.243	0.242	0.255	0.238
Hansen J p-value	0.326	0.328	0.345	0.322

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

investigates whether the relationship between energy market uncertainty, ESG performance, and corporate financial stability is altered during the COVID-19 shock by combining a full-sample interaction model with period-specific subsample regressions. This approach allows the analysis to disentangle structural effects from crisis-driven dynamics and to assess whether ESG-related resilience is state-dependent.

In the full-sample specification, the coefficient on lagged financial stability remains positive and highly significant, confirming the persistence of firm-level financial resilience even in the presence of an unprecedented global shock. Energy market uncertainty continues to exert a statistically significant and economically meaningful negative effect on financial stability. Importantly, the interaction between energy market uncertainty and the COVID dummy is negative and weakly significant, indicating that the destabilizing effect of energy uncertainty was amplified during the pandemic. By contrast, the standalone COVID dummy is statistically insignificant, suggesting that it is not the pandemic per se, but rather its interaction with market uncertainty, that drives additional financial fragility.

Consistent with the baseline and moderation results, ESG performance remains positively associated with financial stability, and the EMU × ESG interaction term is positive and significant, implying that ESG engagement continues to mitigate the adverse impact of energy market uncertainty. Although the triple interaction term (EMU × ESG × COVID) is positive but statistically insignificant, its sign suggests that the stabilizing role of ESG was not weakened during the crisis.

The subsample regressions provide further insight. During the COVID period (2020–2021), the negative effect of energy market uncertainty is strongest in magnitude, highlighting the heightened vulnerability of firms to energy-related shocks under conditions of extreme macroeconomic stress. At the same time, the moderating effect of ESG performance appears slightly stronger during the pandemic than in the pre-COVID period, indicating that ESG-oriented firms were better positioned to absorb compounded shocks. In the post-COVID period, the adverse effect of energy uncertainty moderates, while the positive role of ESG performance remains robust.

Overall, these findings underscore that energy market uncertainty became particularly destabilizing during COVID-19, while ESG performance consistently enhanced financial resilience across crisis and non-crisis periods, reinforcing the view of ESG as a strategic buffer in times of systemic disruption.

4.5.8. Alternative Estimation Methods

Table 13. Robustness test: Alternative estimation methods.

Variable	Model (1)	Model (2)	Model (3)
	Difference GMM	2SLS	FE + Driscoll–Kraay SEs
Lagged Z-Score	0.40 ***	0.39 ***	0.41 ***
	(0.05)	(0.05)	(0.05)
EMU	−0.67 ***	−0.69 ***	−0.65 ***
	(0.12)	(0.11)	(0.12)
ESG	0.19 **	0.20 **	0.21 **
	(0.08)	(0.08)	(0.07)
EMU × ESG	0.16 **	0.17 **	0.18 **
	(0.07)	(0.07)	(0.06)
Controls Variables	Yes	Yes	Yes
Firm Fixed Effects	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes
AR(1) p-value	0.004	–	–
AR(2) p-value	0.245	–	–
Hansen J p-value	0.326	–	–

Notes: Robust standard errors in parentheses. *, **, *** denote significance at the 10%, 5%, and 1% levels.

presents additional robustness checks using alternative econometric techniques—difference GMM, two-stage least squares (2SLS), and fixed effects estimation with Driscoll–Kraay standard errors—to ensure that the main findings are not driven by a specific estimation strategy. These approaches address endogeneity and serial correlation concerns through different identification mechanisms, thereby strengthening the credibility of the empirical results.

Across all three models, the coefficient on the lagged financial stability variable remains positive and highly significant, reaffirming the dynamic persistence of firm-level financial stability. Most importantly, energy market uncertainty continues to exhibit a negative and statistically significant effect on financial stability, with coefficient magnitudes closely aligned with those obtained from the baseline system GMM estimations. This consistency indicates that the destabilizing impact of energy market uncertainty is robust to alternative treatments of endogeneity and dynamic bias.

Similarly, ESG performance maintains a positive and statistically significant association with financial stability across all estimation methods. The interaction term between energy market uncertainty and ESG performance also remains positive and significant, confirming that ESG engagement systematically mitigates the adverse effects of energy market uncertainty on firms’ financial resilience. The stability of both the sign and magnitude of this interaction term across different GMM, 2SLS, and FE–Driscoll–Kraay estimations provides strong support for the moderating role of ESG performance.

Overall, the convergence of results across fundamentally different econometric frameworks confirms that the core conclusions of the study are method-invariant, reinforcing the robustness of the evidence that energy market uncertainty undermines corporate financial stability, while ESG performance plays a stabilizing and buffering role.

5. Conclusions and Policy Implications

5.1. Conclusions

This study examines the dynamic relationship between energy market uncertainty, ESG performance, and corporate financial stability using a panel of Australian non-financial firms over the period 2011–2023. Employing two-step system GMM estimations, the study explicitly accounts for persistence in financial stability, firm-specific heterogeneity, and endogeneity concerns, thereby offering robust and policy-relevant evidence.

The findings provide strong and consistent support for the central hypothesis that energy market uncertainty undermines corporate financial stability. Across all baseline specifications, heightened energy uncertainty exerts a statistically significant and economically meaningful negative effect on firms’ financial resilience, confirming that volatile energy markets amplify cost uncertainty, disrupt cash flows, and elevate financial risk. This effect is particularly relevant for an energy-exposed economy such as Australia, where firms are deeply integrated into global commodity cycles. The persistence of financial stability, reflected in the significant lagged dependent variable, further underscores the importance of adopting a dynamic perspective when assessing firms’ vulnerability to external shocks.

Crucially, the study demonstrates that ESG performance plays a dual role in shaping financial outcomes. First, ESG engagement is positively associated with financial stability, indicating that sustainability-oriented firms benefit from superior risk management, enhanced transparency, and stronger stakeholder relationships. Second, and more importantly, ESG performance significantly moderates the adverse impact of energy market uncertainty. Firms with stronger ESG profiles experience a markedly weaker deterioration in financial stability in response to energy volatility, providing compelling evidence that ESG functions as an effective shock-absorbing mechanism. These findings align with stakeholder theory, resource-based views, and risk-management perspectives, which emphasize the role of non-financial capabilities in enhancing organizational resilience.

A wide range of robustness analyses reinforces the credibility and generalizability of these conclusions. The results remain stable when alternative measures of financial stability and energy market uncertainty are employed, when different lag structures are introduced, and when alternative estimation techniques—including difference GMM, instrumental-variable approaches, and fixed-effects models with Driscoll–Kraay standard errors—are used. Importantly, decomposing ESG into its environmental, social, and governance components reveals that no single pillar drives the moderating effect; rather, environmental efficiency, social capital, and strong governance structures jointly contribute to firms’ ability to withstand energy-related shocks. Subsample analyses further highlight pronounced asymmetries: low-ESG firms exhibit substantially higher sensitivity to energy market uncertainty, whereas high-ESG firms display significantly greater resilience, suggesting that sustainability engagement must reach a critical threshold to be effective.

Additional heterogeneity analyses provide further insight. Sectoral results show that while energy market uncertainty negatively affects all industries, the impact is strongest for energy-intensive firms, reflecting their direct exposure to input price volatility. Nevertheless, ESG performance consistently enhances financial stability across sectors, with particularly strong buffering effects observed in knowledge- and technology-intensive industries. Finally, the COVID-19 analysis reveals that the destabilizing effect of energy market uncertainty intensifies during periods of systemic stress, yet ESG performance continues to mitigate financial fragility before, during, and after the crisis, underscoring its role as a strategic resilience mechanism under both normal and extreme conditions.

Overall, this study offers robust empirical evidence that energy market uncertainty constitutes a persistent threat to corporate financial stability, while ESG performance emerges as a powerful and resilient strategic buffer. By demonstrating how sustainability practices interact with macro-level energy shocks, the findings advance the literature on corporate resilience and provide a compelling case for integrating ESG considerations into firms’ long-term risk management and strategic decision-making frameworks.

5.2. Policy Implications

The findings of this study carry important policy implications for regulators, investors, and corporate decision-makers operating in environments characterized by heightened energy market volatility and accelerating sustainability transitions. First, the robust negative effect of energy market uncertainty on corporate financial stability highlights the need for policymakers to explicitly recognize energy-related risks as a systemic financial concern. Regulatory authorities and central banks—particularly in energy-exposed economies such as Australia—should incorporate energy market uncertainty into macroprudential surveillance frameworks and stress-testing exercises. Doing so would improve the early identification of vulnerabilities arising from commodity price volatility and enhance the resilience of the corporate sector to external energy shocks.

Second, the evidence that ESG performance enhances financial stability and significantly mitigates the destabilizing effects of energy market uncertainty suggests that sustainability should be viewed not merely as a normative or ethical objective, but as a core component of financial risk management. Regulators and standard-setting bodies can leverage this insight by promoting more consistent, comparable, and decision-useful ESG disclosure requirements. Strengthening ESG reporting standards—particularly those related to environmental exposure, energy efficiency, and governance quality—would reduce information asymmetries and enable investors and lenders to more accurately price firms’ resilience to energy-related risks.

Third, the results have direct implications for capital allocation and investment policy. Institutional investors, asset managers, and banks may benefit from explicitly integrating energy uncertainty and ESG metrics into portfolio construction, credit risk assessment, and lending decisions. The demonstrated buffering role of ESG suggests that firms with stronger sustainability profiles are better positioned to withstand adverse energy shocks, particularly during periods of systemic stress such as the COVID-19 crisis. Consequently, incorporating ESG considerations alongside traditional financial indicators can improve risk-adjusted returns and enhance the stability of financial portfolios.

Fourth, the heterogeneity analyses offer guidance for more targeted and differentiated policy interventions. The heightened sensitivity of energy-intensive firms to energy market uncertainty underscores the importance of sector-specific policies that facilitate energy efficiency investments, technological upgrading, and cleaner production processes. At the same time, the strong moderating role of ESG in knowledge- and technology-intensive sectors indicates that policies supporting innovation, human capital development, and governance quality can amplify firms’ adaptive capacity. Policymakers should therefore avoid one-size-fits-all approaches and instead tailor sustainability and energy policies to sectoral characteristics.

Finally, the persistence of ESG’s stabilizing role across pre-crisis, crisis, and post-crisis periods suggests that sustainability-oriented strategies contribute to long-term corporate resilience rather than offering only short-term protection. Corporate managers are thus encouraged to embed ESG considerations into strategic planning, energy risk management, and capital investment decisions, rather than treating sustainability as a peripheral or compliance-driven activity. From a broader policy perspective, aligning energy transition policies with incentives for genuine ESG integration can simultaneously support financial stability, corporate resilience, and sustainable economic growth.

5.3. Limitations and Future Research

While this study provides robust evidence on the relationship between energy market uncertainty, ESG performance, and corporate financial stability, several limitations should be acknowledged, offering valuable avenues for future research. First, although the analysis employs a comprehensive set of firm-level, market-level, and macroeconomic variables within a dynamic panel framework, the measurement of ESG performance relies on aggregated ESG scores. While widely used and empirically validated, composite ESG indices may mask heterogeneity in firms’ underlying sustainability practices and may be subject to differences in disclosure quality across firms and over time. Future studies could exploit more granular ESG indicators, alternative rating providers, or text-based measures derived from sustainability reports to better capture qualitative differences in ESG engagement.

Second, this study focuses on publicly listed non-financial firms in Australia, which, while providing a relevant and energy-exposed setting, may limit the generalizability of the findings to other institutional contexts. Corporate governance structures, regulatory regimes, and energy market characteristics vary substantially across countries. Future research could extend the analysis to cross-country or multi-region samples, particularly comparing developed and emerging economies, to assess whether the stabilizing role of ESG is conditioned by institutional quality, financial market development, or energy dependence.

Third, although the use of system GMM mitigates endogeneity concerns arising from reverse causality, omitted variables, and dynamic persistence, no empirical approach can fully eliminate all sources of endogeneity. Unobserved managerial characteristics, firm culture, or strategic orientation may jointly influence ESG engagement and financial stability. Future studies could employ quasi-natural experiments—such as exogenous regulatory changes, energy policy reforms, or ESG disclosure mandates—or leverage instrumental variables linked to regional or industry-level sustainability shocks to strengthen causal inference.

Fourth, the study captures energy market uncertainty using global and commodity-based indices, which may not fully reflect firm-specific exposure to energy risks. Firms differ substantially in their energy intensity, hedging strategies, and contractual arrangements. Future research could incorporate firm-level energy consumption data, hedging activity, or supply chain exposure to examine how micro-level energy risk management interacts with ESG performance to shape financial outcomes.

Finally, this study emphasizes financial stability as the primary outcome variable, focusing on insolvency risk and earnings volatility. While highly relevant, financial stability represents only one dimension of corporate performance. Future research could explore how energy market uncertainty and ESG performance jointly affect investment efficiency, innovation, employment stability, or long-term firm value.

Overall, these limitations highlight promising directions for future inquiry and underscore the potential for continued research at the intersection of energy economics, corporate sustainability, and financial stability.