The Impact of ESG Performance on Financial Performance: Evidence from Listed Companies in Thailand

Highlights

What are the main findings?

• ESG performance shows a significant negative short-term effect on ROA, while its relationship with ROCE is statistically insignificant for Thai listed firms.
• Financial leverage consistently and strongly reduces firm performance, emerging as the dominant determinant of both ROA and ROCE.

What are the implications of the main findings?

• ESG initiatives in emerging markets may involve short-term profitability trade-offs, requiring a long-term strategic perspective from managers and investors.
• Policymakers and firms should prioritize financial structure discipline and supportive ESG governance mechanisms to enable sustainable value creation over time.

Abstract

Sustainable corporate governance plays an essential role in promoting responsible economic growth and enhancing social and environmental well-being in emerging economies. In this context, Environmental, Social, and Governance (ESG) performance has become an important indicator of a firm’s commitment to sustainable development and its alignment with the United Nations Sustainable Development Goals, particularly SDG 8 and SDG 12. This study investigates the impact of Environmental, Social, and Governance (ESG) performance on the financial sustainability of publicly listed companies in Thailand, a rapidly developing Southeast Asian economy where empirical evidence remains limited. Using an unbalanced panel dataset of 965 firm-year observations across multiple industries, multiple regression models were employed to assess the influence of ESG performance on two financial indicators: return on capital employed and return on assets. Granger causality tests were also conducted to explore directional relationships between sustainability performance and financial outcomes. The empirical results reveal a significant negative short-term association between ESG performance and return on assets (ROA), whereas the relationship with return on capital employed (ROCE) is statistically insignificant. The causality analysis indicates that ESG performance Granger-causes ROA, implying that sustainability-driven strategic decisions may precede and influence financial outcomes over time. Additionally, leverage emerges as a key constraint to financial sustainability, negatively affecting both ROCE and ROA. These findings underscore the challenge of striking a balance between sustainability investments and immediate profitability in emerging markets. Policymakers and business leaders are encouraged to promote supportive governance frameworks, reduce financial barriers, and foster ESG-driven practices that contribute to long-term sustainable competitiveness and inclusive development.

1. Introduction

In 2024, global sustainable investment surpassed USD 40 trillion, reflecting investors’ growing demand for responsible and ethical corporate behavior [1]. Yet, a fundamental question persists: Do companies that perform well on Environmental, Social, and Governance (ESG) dimensions also achieve superior financial performance? This question is especially critical as corporations face mounting pressure from regulators, consumers, and investors to integrate sustainability into their core strategies amid volatile economic conditions and intensifying climate challenges [2,3].

ESG performance has evolved from a voluntary reporting framework to a core determinant of corporate competitiveness and resilience. Numerous studies have explored the link between ESG and financial outcomes, revealing both converging and conflicting findings. Some scholars argue that ESG engagement enhances firms’ reputations, attracts long-term investors, and improves risk management, thereby contributing to higher profitability [4]. Others, however, report mixed or even negative relationships, attributing discrepancies to methodological differences, regional variations, and the short-term costs of ESG implementation [5,6]. These inconsistencies underscore the complexity of the ESG–financial performance nexus and the importance of contextualizing this relationship within specific national and institutional settings.

A growing body of empirical research has examined the relationship between Environmental, Social, and Governance (ESG) performance and firm financial outcomes across different markets. Recent studies report mixed evidence, with some documenting positive effects of ESG on profitability and firm value (e.g., Velte [7]; Chen et al., [8]; Almaqtari et al., [9]), while others identify neutral or negative short-term impacts, particularly in emerging economies where sustainability investments may impose adjustment costs (Marasigan, [10]; Devi and Sapna, [11]). Much research further highlights the role of institutional quality, governance structures, and market maturity in shaping the ESG–performance nexus (Gerged, [12]; Kong et al., [13]; Michalski, [14]).

Despite this expanding literature, three important gaps remain. First, empirical evidence from Southeast Asian capital markets, particularly Thailand, is still limited and fragmented. Second, most studies rely on contemporaneous correlations and do not explicitly investigate the predictive or causal direction of ESG performance relative to accounting-based indicators such as return on assets and return on capital employed. Third, few studies interpret ESG as a forward-looking signal within a forecasting framework, which is essential for investors and policymakers concerned with the dynamic financial implications of sustainability strategies.

In emerging markets, the dynamics of ESG adoption differ markedly from those in developed economies. Prior studies have largely focused on mature markets with well-established regulatory systems and stakeholder awareness, such as the United States and Western Europe [2]. Thailand, in particular, presents a compelling case. The Stock Exchange of Thailand (SET) has actively encouraged sustainability disclosures through its ESG rating system and sustainability awards, yet whether ESG performance translates into tangible financial gains for listed Thai companies remains empirically underexplored.

Furthermore, methodological limitations persist across the literature. Many studies rely on aggregate ESG indices without addressing potential multicollinearity among the three ESG pillars, which can obscure the unique effects of environmental, social, and governance practices [6]. Others fail to consider causality, often assuming that ESG initiatives drive performance rather than recognizing the possibility of reverse or bidirectional relationships. These gaps hinder both theoretical advancement and practical decision-making for investors and corporate leaders in emerging economies.

This study investigates the influence of Environmental, Social, and Governance (ESG) performance on the financial performance of publicly listed firms in Thailand, addressing existing methodological and contextual gaps in the literature. While previous research has extensively examined the ESG–financial performance nexus in developed economies [15,16,17], empirical evidence from Southeast Asian markets remains limited and fragmented. By employing an aggregated analysis of ESG pillars and utilizing robust econometric modeling, this study seeks to determine the extent to which ESG engagement contributes to firms’ financial outcomes within Thailand’s rapidly developing market context. Specifically, it examines the relationship between ESG performance and financial performance, measured by Return on Capital Employed (ROCE) and Return on Assets (ROA). It evaluates the presence of causal linkages using Granger causality tests [18]. In addition, the study identifies other key determinants of financial performance, such as firm size and leverage [19], to provide a more comprehensive understanding of the financial implications of ESG strategies. The findings are expected to extend existing ESG-performance frameworks to emerging economies, offering practical insights for policymakers, investors, and corporate managers seeking to align sustainability initiatives with financial objectives [20,21].

This study makes three main contributions to the literature. First, it provides new empirical evidence on the ESG–financial performance nexus in Thailand, an emerging market that remains underrepresented in prior research. Second, by employing panel regression and Granger causality analysis, the study goes beyond contemporaneous associations and examines the predictive direction of ESG performance relative to accounting-based indicators, namely return on assets and return on capital employed. Third, by interpreting ESG performance as a potential leading indicator of firm profitability, the analysis contributes to the forecasting-oriented literature and offers practical implications for investors, corporate managers, and policymakers concerned with the dynamic financial consequences of sustainability strategies in developing economies.

Beyond examining contemporaneous associations, this study aims to (i) assess the dynamic and predictive role of ESG performance for firm-level profitability and capital efficiency, (ii) account for structural time effects associated with the COVID-19 crisis and post-pandemic recovery, and (iii) interpret the ESG–performance nexus through the lens of ESG maturity and capital structure in an emerging market setting. By integrating panel econometric modeling, time effects, and Granger-based predictability analysis, the study goes beyond descriptive correlation and contributes to the forecasting-oriented literature on sustainability and firm performance. In addition to examining contemporaneous and causal relationships, this study evaluates whether ESG performance improves the out-of-sample predictability of firm profitability, thereby contributing to the forecasting literature by assessing the incremental predictive content of sustainability indicators.

2. Literature Review

2.1. Theoretical Perspectives on ESG and Financial Performance

Stakeholder Theory, originally proposed by Freeman [22], posits that firms create long-term value by balancing the interests of multiple stakeholder groups rather than focusing solely on shareholders. In its original formulation, the theory emphasizes legitimacy, trust, and cooperative relationships as sources of competitive advantage. In the ESG context, this perspective has been widely used to argue that environmental responsibility, social engagement, and sound governance enhance firm reputation, reduce stakeholder conflict, and ultimately improve financial performance [7,21].

The Resource-Based View (RBV), developed by Barney [23], conceptualizes competitive advantage as arising from valuable, rare, inimitable, and non-substitutable firm resources. In the context of ESG, sustainability capabilities, governance quality, and stakeholder relationships are considered strategic intangible assets that can enhance operational efficiency and risk management, thereby supporting superior financial outcomes [8,13].

Legitimacy Theory, rooted in Suchman’s work [24], argues that organizations seek alignment with societal norms to secure continued access to resources and social acceptance. In ESG research, this framework explains why firms engage in sustainability reporting and responsible practices to maintain legitimacy in the eyes of regulators, investors, and the public, thereby lowering capital costs and stabilizing long-term performance [12,25].

In contrast, Agency Theory [26] views managers as potentially pursuing objectives that diverge from shareholder value maximization. From this perspective, ESG investments may reflect managerial preferences or reputational concerns that impose short-term costs and reduce profitability, particularly when sustainability expenditures are not aligned with firm strategy [10,27,28].

2.2. Empirical Evidence: Mixed and Evolving Findings

Empirical studies on ESG’s impact on financial performance remain inconclusive despite extensive scholarly attention. A meta-analysis of over 2000 studies revealed that approximately 90% reported either positive or neutral effects, implying that ESG initiatives generally support financial outcomes [15]. Similarly, Eccles et al. [21] demonstrated that high-sustainability firms outperformed low-sustainability peers in stock returns and accounting performance over an 18-year period, reinforcing the long-term benefits of ESG engagement.

However, early research by Aupperle et al. [27] and McWilliams and Siegel [28] found weak or negative relationships, suggesting that ESG investments might impose short-term financial burdens due to compliance and implementation costs. More recent findings indicate that ESG performance affects financial indicators unevenly: while return on assets (ROA) tends to respond positively, market-based indicators such as Tobin’s Q often show weaker or insignificant effects [29]. Studies by Chen et al. [2] and Ahmad et al. [30] confirm that firm size moderates this relationship, with large firms exhibiting stronger positive ESG–performance links due to superior resources and reputational capital.

Conversely, Marasigan [10] found that ESG practices negatively influenced the financial performance of ASEAN-listed banks, highlighting potential regional heterogeneity. Zahroh and Hersugondo [31] observed that in Indonesia, environmental scores had insignificant effects compared to social and governance dimensions, while Devi and Sapna [11] reported a negative linkage between ESG ratings and firm performance in Indian companies. These contradictory results reveal that ESG’s financial implications are context-dependent and influenced by differences in regulatory maturity, data quality, and time horizons.

2.3. Integrating ESG and Corporate Strategy

Recent research increasingly views ESG not as a peripheral compliance activity but as an integrated component of business strategy. Between 2020 and 2023, firms primarily approached ESG as a tool for risk management and ethical compliance. Chen et al. [8] found that ESG engagement initially reduced short-term profitability due to resource diversion but improved long-term value through enhanced reputation and stakeholder trust. Almaqtari et al. [9] confirmed that ESG variables correlate more strongly with long-term valuation metrics (e.g., Tobin’s Q) than with short-term market returns, while Kong et al. [13] emphasized ESG’s strategic role in sustaining competitive advantage within technology-driven sectors.

A paradigm shift occurred post-2024, with studies exploring ESG as a core strategic capability. Michalski [14] proposed integrating ESG into the Balanced Scorecard (BSC) to align sustainability with performance management. Gazzola et al. [32] showed how manufacturers embed UN SDG-aligned goals into business models, while Pujiyono et al. [33] demonstrated that ESG-oriented models enhance supply chain resilience and profitability in the Indonesian paint industry. Despite these advancements, much of the literature remains quantitative, emphasizing correlations over mechanisms. There is limited qualitative evidence on how firms operationalize ESG principles, revealing a gap that future research should fill through longitudinal or process-oriented analyses.

2.4. Global Disclosure Standards: IFRS S1 and S2 Compliance

The institutionalization of sustainability disclosure has accelerated since the establishment of IFRS S1 and S2 standards by the International Sustainability Standards Board (ISSB) in 2023 [34]. These frameworks mandate that firms disclose how sustainability-related risks influence strategic and financial decisions. While some studies argue that these standards enhance cross-border comparability [35], others caution that differing national interpretations and resource capacities create uneven implementation outcomes [36,37].

Empirical evidence further highlights this disparity: Milhem [38] reported that Palestinian firms’ sustainability disclosures fall below international benchmarks, while Pratama et al. [37] showed that small and medium enterprises struggle to meet IFRS-aligned standards due to technological and financial constraints. Market-based studies suggest mixed investor reactions—Dwiyandi [39] found positive market responses to risk-management disclosures but negative reactions to strategy-related transparency, whereas Yunita [25] observed that strong ESG and climate disclosures enhance firm value. These findings reveal that institutional readiness, not merely standard adoption, determines ESG disclosure effectiveness—a critical issue for emerging markets like Thailand.

2.5. Governance as a Core Enabler of ESG Performance

Governance functions as the structural foundation for effective ESG implementation. Strong boards ensure transparency, accountability, and consistency in ESG decision-making [12,40]. Amore and Bennedsen [41] demonstrated that weak governance structures correlate with lower innovation in environmental technologies, whereas Gerged et al. [42] confirmed that board diversity and environmental committees improve ESG disclosure quality. Similarly, studies across Africa and Asia found that gender-diverse boards correlate positively with sustainability reporting [43,44,45].

While these studies highlight the benefits of inclusive and independent governance, most are limited to cross-sectional analyses, leaving causal relationships underexplored. Moreover, the interaction between governance and other ESG pillars—such as how leadership diversity influences environmental or social outcomes—remains underexamined. Addressing this gap would enhance the understanding of governance as both an enabler and mediator in ESG–performance relationships.

2.6. ESG in Emerging Markets: Contextual and Methodological Gaps

ESG research in emerging economies, including Thailand, remains comparatively underdeveloped. Weak institutional enforcement, lower stakeholder activism, and inconsistent disclosure standards create barriers to effective ESG integration [46]. Some evidence suggests that ESG initiatives attract foreign investment and signal adherence to global norms, yet others argue they function as “luxury goods” affordable only to financially stable firms, implying potential reverse causality [47].

Thailand’s policy environment—driven by the Stock Exchange of Thailand’s sustainability awards and ESG rating systems—provides a fertile ground to test these relationships. However, limited empirical evidence exists regarding how ESG scores translate into financial performance across sectors. Most prior studies rely on aggregated ESG indices, overlooking the potential multicollinearity among the environmental, social, and governance pillars, which may bias coefficient estimates and obscure the distinct effects of each dimension [7,13,37]. Furthermore, few studies employ causal inference techniques such as instrumental variables, dynamic panel models, or panel-based Granger causality tests, leaving the directionality between ESG and financial performance ambiguous and raising concerns about endogeneity and reverse causality [8,29,48].

2.7. Synthesis and Future Research Directions

The existing literature collectively suggests that ESG performance and financial performance are interrelated, but the magnitude and direction of this relationship depend on context, time horizon, and governance quality. Theoretical models provide strong justification for ESG as a strategic resource, yet empirical studies remain fragmented and methodologically inconsistent. Future research should therefore focus on:

• Disaggregated ESG analysis, distinguishing between environmental, social, and governance effects rather than relying on composite scores.
• Causal modeling approaches, such as dynamic panel estimation or difference-in-differences, to clarify the direction of impact.
• Institutional and cultural contextualization, particularly in Southeast Asia, where ESG practices are evolving within distinct regulatory and market conditions.
• Qualitative or mixed-method approaches to uncover the mechanisms through which ESG is integrated into corporate strategy and decision-making.

By addressing these gaps, future studies can deepen theoretical understanding and offer practical insights into how ESG investments contribute not only to sustainable development but also to financial resilience in emerging markets such as Thailand.

Taken together, the theoretical perspectives reviewed in Section 2.1, Section 2.2, Section 2.3, Section 2.4, Section 2.5 and Section 2.6 provide a coherent yet tension-filled framework for analyzing the ESG–financial performance nexus. Stakeholder Theory and the Resource-Based View predict a positive relationship, as ESG engagement enhances legitimacy, stakeholder trust, and the accumulation of valuable intangible resources that may improve operational efficiency and long-term competitiveness. Legitimacy Theory further emphasizes the role of ESG practices in securing institutional acceptance and reducing regulatory and reputational risk. In contrast, Agency Theory highlights the possibility that ESG investments generate short-term costs or reflect managerial preferences that may not immediately translate into shareholder value. These complementary and competing arguments imply that the net financial effect of ESG is context-dependent and may vary across institutional environments and stages of sustainability maturity. Building on this integrated theoretical foundation, the present study formulates hypotheses at the intersection of these perspectives and empirically tests whether ESG performance, firm size, and leverage jointly shape capital efficiency and profitability in the Thai emerging market context.

2.8. Hypotheses Development

While ESG performance constitutes the central sustainability construct in this study, prior literature suggests that the financial implications of ESG initiatives do not arise in isolation but depend on firms’ structural and financial conditions. In particular, organizational resources and financial constraints influence a firm’s capacity to invest in, implement, and sustain ESG-related strategies. Larger firms often possess greater managerial capacity, access to capital, and stakeholder visibility, which can facilitate ESG integration. Conversely, highly leveraged firms may face tighter financial constraints and risk pressures that limit their ability to convert ESG investments into financial gains. Therefore, firm size and leverage are conceptualized in this study as conditioning factors that shape the strength and direction of the ESG–financial performance relationship, rather than as alternative sustainability constructs. Based on the theoretical framework and empirical evidence, we hypothesize that:

RQ1. 

To what extent does Environmental, Social, and Governance (ESG) performance, as a proxy for firms’ alignment with the Sustainable Development Goals, affect capital efficiency in Thai listed companies?

H1. 

ESG performance has a positive effect on Return on Capital Employed (ROCE).

Rationale: Companies with better ESG performance are expected to utilize capital more efficiently through improved operational practices, reduced risks, and enhanced stakeholder relationships.

RQ2. 

To what extent does Environmental, Social, and Governance (ESG) performance, reflecting firms’ commitment to sustainable development, influence profitability in Thai listed companies?

H2. 

ESG performance has a positive effect on Return on Assets (ROA).

Rationale: ESG initiatives can lead to cost savings, revenue enhancement, and risk mitigation, thereby improving asset utilization and profitability.

In addition to examining the direct relationship between ESG performance and financial outcomes (linked to SDGs 8 and 12), this study also investigates firm-specific structural conditions that may enable or constrain the realization of sustainability-related economic benefits. In particular, firm size and financial leverage are key factors influencing a firm’s capacity to invest in, implement, and benefit from ESG-oriented strategies, thereby indirectly shaping progress toward the SDGs.

RQ3. 

How does firm size, as a proxy for organizational capacity and resource availability, condition the relationship between ESG performance and firms’ financial performance?

H3. 

The positive relationship between ESG performance and financial performance is stronger for larger firms.

Rationale: Larger firms benefit from economies of scale, greater market power, and access to resources.

RQ4. 

How does financial leverage, as an indicator of financial constraints and risk exposure, condition the relationship between ESG performance and firms’ financial performance?

H4. 

Financial leverage weakens the relationship between ESG performance and financial performance.

Rationale: Higher financial leverage increases financial risk, interest expenses, and the probability of financial distress, negatively impacting profitability.

3. Research Methodology

3.1. Data and Sample

This study employs panel data comprising 965 firm-year observations from companies listed on the Stock Exchange of Thailand (SET) during the period 2020–2024. The multi-industry sample design ensures comprehensive representation of Thailand’s corporate landscape, consistent with prior ESG-finance studies that emphasize cross-sectoral generalizability [2,15].

Data were collected from three main sources. Financial indicators (e.g., total assets, liabilities, and net income) were extracted from annual reports and audited financial statements published by listed firms. Market data were obtained from the SET’s public financial database, while ESG ratings were derived from the CRISIL ESG Rating Methodology [49], which provides a standardized, comparable measure of firms’ sustainability performance. Using CRISIL’s composite ESG ratings ensures methodological consistency and cross-country comparability aligned with recent empirical frameworks [4].

All firms included in the final sample had complete information for the variables required to compute ESG scores, return on assets, return on capital employed, firm size, and leverage for at least one year during the study period. Observations with missing ESG ratings or incomplete financial statements were excluded from the analysis, resulting in an unbalanced panel of 965 firm-year observations. No interpolation or imputation was applied in order to avoid introducing measurement bias, and only actual reported values from audited financial statements and the ESG database were used.

3.2. Variable Definition and Measurement

3.2.1. Dependent Variables

Two accounting-based financial performance measures were selected to capture profitability and capital efficiency dimensions.

(1)

Return on Capital Employed (ROCE):

ROCE assesses the efficiency with which a firm utilizes its capital to generate operating profits and is computed as:

ROCE_it = EBIT_it/Capital Employed_it

(1)

where

Capital Employed_it = Total Assets_it − Current Liabilities_it

(2)

Furthermore, EBIT denotes earnings before interest and tax, and Capital Employed equals total assets minus current liabilities. ROCE has been widely used in sustainability–finance studies for evaluating operational performance [7], and measures operational efficiency in utilizing invested capital.

(2)

Return on Assets (ROA):

ROA reflects a firm’s profitability relative to its asset base and is calculated as:

ROA_it = Net Income_it/Total Assets_it

(3)

ROA represents profitability relative to total assets, providing a stable performance measure that minimizes capital-structure distortions and is frequently used to assess the ESG–profitability nexus [30].

3.2.2. Independent Variable

(1)

ESG Performance (ESG):

The overall ESG score was derived following the CRISIL [49] weighting structure: Environmental (35%), Social (25%), and Governance (40%). The composite score is computed as:

ESG_it = (0.35 × E_it) + (0.25 × S_it) + (0.40 × G_it)

(4)

where

• E_it = Environmental score,
• S_it = Social score, and
• G_it = Governance score.

The weights follow the [49] methodology, emphasizing the governance dimension.

Each dimension was rated on a five-point scale ranging from 1 (very poor) to 5 (excellent) ESG performance. The higher weight assigned to governance reflects its critical role in driving sustainable environmental and social outcomes [42]. Although CRISIL is headquartered in India, its ESG rating methodology is based on internationally harmonized disclosure standards (e.g., GRI, SASB, TCFD, and ISSB-aligned indicators) and is applied consistently to multinational enterprises and firms across emerging markets, including the ASEAN region. Therefore, the CRISIL framework provides a comparable, internationally recognized measure of ESG performance that is appropriate for the analysis of Thai-listed companies.

3.2.3. Control Variables

Two control variables were incorporated to mitigate firm-specific heterogeneity and enhance model robustness.

(1)

Firm Size (SIZE):

Firm size was measured as the natural logarithm of total assets, consistent with previous financial performance studies [50].

SIZE_it = ln(Total Assets_it)

(5)

Firm size is expressed as the natural logarithm of total assets to reduce skewness in scale.

(2)

Leverage (LEV):

Leverage, representing financial risk and capital structure, was defined as the ratio of total liabilities to total assets [51].

LEV_it = Total Liabilities_it/Total Assets_it

(6)

Leverage reflects the firm’s capital-structure risk.

3.3. Analytical Approach

3.3.1. Descriptive and Diagnostic Analysis

Descriptive statistics, including mean, standard deviation, minimum, and maximum values, were computed for all variables to understand their distributional characteristics. Diagnostic tests were performed prior to regression analysis to ensure data quality and validity.

(1)

Mean

$$\overline{X}={\displaystyle \frac{1}{n}}\sum _{i=1}^n{X}_i$$

(7)

where $\overline{X}$ is the sample mean, $X_i$ represents each observation, and n is the number of observations.

(2)

Standard Deviation

$$s=\sqrt{{\displaystyle \frac{1}{n-1}}\sum _{i=1}^n{(X_i-\overline{X})}^2}$$

(8)

This statistic measures the dispersion of values around the mean.

(3)

Minimum and Maximum Values

X_min = min(X₁, X₂, …, X_n), …X_max = max(X₁, X₂, …, X_n)

(9)

These provide the range boundaries for each variable’s observed values.

An Augmented Dickey–Fuller (ADF) test was employed to examine the presence of unit roots and confirm data stationarity—an essential condition for reliable panel estimation.

(4)

Stationarity Test: Augmented Dickey–Fuller (ADF)

The ADF test examines whether a time-series variable has a unit root (non-stationarity). The regression form is:

ΔY_t = α + βt + γY_t−1 + Σ(i = 1 to p) δ_iΔY_t−i + ε_t

(10)

where

• ΔY_t = Y_t − Y_t−1 denotes the first difference,
• α is a constant,
• βt represents the deterministic trend, and
• ε_t is a white-noise error term.

If the null hypothesis H₀: γ = 0 is not rejected, the series is non-stationary (i.e., has a unit root).

Additionally, multicollinearity diagnostics using the Variance Inflation Factor (VIF) and tolerance values were conducted. Following Hair et al. [52], VIF values below 10 and tolerance above 0.10 were considered acceptable.

(5)

Variance Inflation Factor (VIF)

VIF_j = 1/(1 − R²_j)

(11)

where R²_j is the coefficient of determination obtained by regressing predictor X_j on all other predictors.

A VIF < 10 indicates an acceptable level of multicollinearity [52].

(6)

Tolerance

Tolerance_j = 1 − R²_j

(12)

A tolerance value above 0.10 suggests no severe multicollinearity.

3.3.2. Bivariate Correlation Analysis

Pearson correlation coefficients were calculated to explore bivariate relationships among variables and detect potential collinearity prior to multivariate modeling. This step helps validate the conceptual consistency between ESG scores, firm size, leverage, and financial outcomes [15].

Pearson Correlation Coefficient

$${\mathrm{r}}_{\mathrm{x}\mathsf{\gamma }}=\Sigma (\mathrm{i}=1\mathrm{to}\mathrm{n})({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}})({\mathrm{Y}}_{\mathrm{i}}-\overline{\mathrm{Y}})/\surd [{\Sigma (\mathrm{i}=1\mathrm{to}\mathrm{n})({\mathrm{X}}_{\mathrm{i}}-\overline{\mathrm{X}})}^2{\Sigma (\mathrm{i}=1\mathrm{to}\mathrm{n})({\mathrm{Y}}_{\mathrm{i}}-\overline{\mathrm{Y}})}^2]$$

(13)

where

• r_xγ measures the strength and direction of the linear relationship between X and Y,
• $\overline{\mathrm{X}}$ and $\overline{\mathrm{Y}}$ denote the sample means of variables X and Y, respectively.

Correlation coefficients range between −1 and +1, indicating perfect negative and perfect positive linear relationships, respectively.

3.3.3. Panel Regression Models

To test the hypothesized relationships, panel regression analysis was conducted using both fixed-effects (FE) and random-effects (RE) estimations, with model selection guided by the Hausman specification test. To control for time-specific macroeconomic shocks and structural breaks (e.g., the COVID-19 pandemic and post-pandemic recovery), year dummy variables were included in the panel regressions. This allows us to isolate the effect of ESG from common temporal shocks affecting all firms.

Two baseline panel regression models were estimated to test the hypothesized relationships between ESG performance and firm-level financial outcomes.

Model 1:

$${ROCE}_{it}={\beta }_1{ESG}_{it}+{\beta }_2{SIZE}_{it}+{\beta }_3{LEV}_{it}+\sum _{t=2021}^{2024}{\delta }_t{D}_t+{\alpha }_i+{\epsilon }_{it}$$

(14)

Model 2:

$${ROA}_{it}={\beta }_0+{\beta }_1{ESG}_{it}+{\beta }_2{SIZE}_{it}+{\beta }_3{LEV}_{it}+\sum _{t=2021}^{2024}{\delta }_t{D}_t+{\epsilon }_{it}$$

(15)

where β₀ is the intercept, β_i represents the regression coefficients, ${\alpha }_i$ is the firm fixed effect; the intercept is absorbed, and ε_it denotes the error term. Panel estimation was preferred over pooled OLS to control for unobserved firm-specific heterogeneity and time-invariant characteristics [53].

3.3.4. Granger Causality Test

To explore potential bidirectional relationships between ESG and financial performance, Granger causality tests were performed using a lag length of two periods. This approach identifies whether past ESG performance helps predict subsequent financial outcomes, or vice versa, thereby addressing the endogeneity concerns highlighted in previous research [54].

To examine bidirectional causality:

Y_t = α₀ + Σ(i = 1 to p) α_iY_t−i + Σ(j = 1 to p) β_jX_t−j + ε_t

(16)

If any β_j ≠ 0, then X Granger-causes Y, indicating predictive influence between ESG and financial performance [54].

The optimal lag length for the Granger causality tests was determined using standard information criteria, including the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), subject to the constraint imposed by the relatively short time dimension of the panel (2020–2024). Both criteria consistently indicated a maximum of two lags as the optimal specification that balances model fit and parsimony while preserving sufficient degrees of freedom. Accordingly, the Granger causality analysis was conducted using up to two annual lags, allowing for delayed adjustment effects of ESG performance on accounting-based financial indicators.

3.4. Ethical and Data Integrity Considerations

All financial and ESG data were obtained from publicly available, verified corporate disclosures and secondary databases. The study adheres to principles of transparency and replicability by applying standardized data sources and well-established econometric procedures [52,53].

While the econometric tools employed in this study (panel regression, fixed effects, and Granger causality tests) are well established, the contribution of the analysis lies in their integrated application to examine the dynamic and predictive relationship between ESG performance and firm-level financial outcomes in an emerging market during a period of structural disruption (COVID-19 and post-pandemic recovery). Accordingly, the methodological focus is placed on inference, temporal effects, and predictive interpretation rather than on the exposition of elementary statistical concepts.

4. Results

4.1. Descriptive Statistics

Table 1. Results of Descriptive Analysis.

Variable	Range	Min	Max	Mean	Std. Dev.
ROCE	2.988	−1.263	1.724	0.103	0.119
ROA	88.040	−31.100	56.940	7.877	7.092
ESG	4.000	1.000	5.000	2.673	1.239
SIZE	9.294	6.037	15.331	9.909	1.968
LEVERAGE	1.124	0.070	1.193	0.480	0.208

presents the descriptive statistics for all variables in the study.

The descriptive statistics presented in Table 1 highlight several key characteristics of the sampled Thai listed firms. The mean Return on Capital Employed (ROCE) is 10.3%, with a standard deviation of 11.9%, suggesting a moderate level of capital utilization efficiency accompanied by substantial variability across companies. The ROCE values range from −1.26 to 1.72, reflecting considerable disparities in performance, where some firms demonstrate weak capital efficiency while others perform exceptionally well. Similarly, the average Return on Assets (ROA) is 7.88%, with a relatively high standard deviation of 7.09% and an extensive range from −31.1% to 56.94%, indicating substantial heterogeneity in profitability. These results imply that while certain firms experienced severe losses, others achieved outstanding financial returns, consistent with prior findings on performance dispersion in emerging markets [19]. The average ESG score of 2.67 out of 5 suggests moderate to low engagement in sustainability practices, with a standard deviation of 1.24 and values spanning all five levels, thereby illustrating diverse ESG adoption patterns among Thai corporations [15]. Firm size, measured as the natural logarithm of total assets, has a mean value of 9.91, equivalent to approximately 20,000 million Baht in assets. The wide range, from 420 million to 4,551,000 million Baht, confirms that the sample adequately represents both small enterprises and large conglomerates within the Thai capital market [19]. The mean leverage ratio stands at 48%, indicating that, on average, firms finance nearly half of their assets through debt. However, the leverage range—from 7% to 119%—reveals considerable variation in financial structure, with some firms maintaining conservative debt policies and others facing elevated financial risk due to debt levels exceeding total assets [51].

4.2. Diagnostic Tests

4.2.1. Stationarity Test

Table 2. Augmented Dickey–Fuller (ADF) Test Results.

Variable	ADF t-Statistic	p-Value	Result
ESG	−5.897	0.000	Stationary
SIZE	−5.060	0.000	Stationary
LEVERAGE	−8.546	0.000	Stationary
ROCE	−11.256	0.000	Stationary
ROA	−13.762	0.000	Stationary

presents the results of the Augmented Dickey–Fuller (ADF) unit root tests, indicating that all variables have ADF t-statistics below their respective critical values and p-values of 0.000. These results lead to the rejection of the null hypothesis of a unit root, thereby confirming the stationarity of all variables. Consequently, the data are deemed appropriate for regression analysis, reducing the likelihood of spurious regression results and ensuring the reliability of subsequent econometric estimations [18].

4.2.2. Multicollinearity Test

As shown in

Table 3. Variance Inflation Factor (VIF) and Tolerance.

Variable	VIF	Tolerance
ESG	1.171	0.854
SIZE	1.673	0.598
LEVERAGE	1.476	0.677

, all variance inflation factor (VIF) values were well below the recommended threshold of 5, while the corresponding tolerance values exceeded 0.50, indicating the absence of multicollinearity among the independent variables. These results confirm that the predictors in the regression model were sufficiently independent of one another, ensuring that the estimated coefficients were both stable and interpretable [52].

4.3. Correlation Analysis

Table 4. Pearson Correlation Matrix.

Variable	ROCE	ROA	ESG	SIZE	LEVERAGE
ROCE	1.000	0.748	−0.083	−0.158	−0.186
ROA	0.748	1.000	−0.138	−0.224	−0.332
ESG	−0.083	−0.138	1.000	0.379	0.172
SIZE	−0.158	−0.224	0.379	1.000	0.566
LEVERAGE	−0.186	−0.332	0.172	0.566	1.000

presents the key findings from the correlation matrix, revealing several notable relationships among the study variables. Return on Capital Employed (ROCE) and Return on Assets (ROA) exhibit a strong positive correlation (r = 0.748), suggesting that both indicators capture closely related dimensions of firm financial performance. Environmental, Social, and Governance (ESG) scores display weak negative correlations with both ROCE (r = −0.083) and ROA (r = −0.138), implying a slight inverse association between ESG engagement and short-term profitability. Leverage demonstrates the strongest negative relationship with financial performance, particularly with ROA (r = −0.332), indicating that higher debt levels may adversely affect returns. Firm size (SIZE) shows a positive correlation with ESG (r = 0.379), suggesting that larger firms generally attain higher ESG ratings, possibly due to greater resources for sustainability initiatives. Additionally, SIZE and LEVERAGE are moderately positively correlated (r = 0.566), indicating that larger firms tend to employ higher levels of debt financing. These interrelationships provide a foundational understanding for subsequent analyses, including the Granger causality test conducted in Section 4.4.

4.4. The Granger Causality Test

Table 5. Granger Causality Test Results.

Cause → Effect	Min p-Value (Lags ≤ 2)	All p-Values	Significance
ESG → ROCE	0.067	[0.067, 0.071]	†
SIZE → ROCE	0.004	[0.007, 0.004]	**
LEVERAGE → ROCE	0.014	[0.040, 0.014]	*
ESG → ROA	0.000	[0.047, 0.000]	***
SIZE → ROA	0.001	[0.003, 0.001]	**
LEVERAGE → ROA	0.011	[0.020, 0.011]	*

Significance levels: *** p < 0.001 (highly significant), ** p < 0.01 (significant), * p < 0.05 (marginally significant), † p < 0.10 (weakly significant).

summarizes the results of the Granger causality test, which evaluates whether past values of one variable contain predictive information for future values of another. In this context, Granger causality does not imply true causation but rather indicates statistical predictability based on temporal precedence. The findings reveal that ESG exhibits weak evidence of Granger causality with ROCE (p = 0.067, marginally significant at the 10% level) but shows very strong evidence with ROA (p < 0.001), suggesting that ESG performance contributes more to predicting asset returns than to capital efficiency. Firm size (SIZE) demonstrates a robust causal effect on both ROCE (p = 0.004) and ROA (p = 0.001), while leverage (LEVERAGE) also Granger-causes both financial performance indicators at the 5% significance level (ROCE: p = 0.014; ROA: p = 0.011). These results confirm the importance of SIZE and LEVERAGE as control variables in financial performance modeling. Moreover, the consistent significance of relationships across lag structures, as indicated by the aggregate p-values, reinforces the stability of these predictive linkages. Overall, the findings imply that ESG, SIZE, and LEVERAGE jointly influence firm performance dynamics, with ESG exerting a more pronounced predictive impact on ROA than on ROCE, consistent with the subsequent regression analyses. The fact that ESG Granger-causes ROA only at a two-period lag suggests that the financial effects of sustainability initiatives are not immediate but materialize with a delay, reflecting the time required for ESG investments to translate into operational improvements, reputational gains, and risk reduction.

4.5. Regression Analysis

4.5.1. Model 1: Impact on Return on Capital Employed (ROCE)

Model 1: ROCE = β₀ + β₁ESG + β₂SIZE + β₃LEVERAGE + ε

(17)

Model Summary:

R-squared: 0.040 (4.0%)|Adj R-squared: 0.037 (3.7%)

F-statistic: 13.262|Prob(F): 1.733 × 10⁻⁸ (<0.001)

Durbin–Watson: 1.210

Table 6. Regression Results for Model 1.

Variable	Coefficient	Std Error	t-Statistic	p-Value	Significance
Constant	0.189310	0.019558	9.680	0.000	***
ESG	−0.003332	0.003298	−1.010	0.313
SIZE	−0.003780	0.002481	−1.524	0.128
LEVERAGE	−0.083247	0.022021	−3.781	0.000	***

Significance levels: *** p < 0.001.

reports the regression results for Model 1, examining the determinants of Return on Capital Employed (ROCE). The overall model is statistically significant (F = 13.262, p < 0.001), confirming that the independent variables collectively explain variation in ROCE; however, its explanatory power remains low, with an R² of only 4.0%. Among the predictors, Environmental, Social, and Governance (ESG) performance shows a negative but statistically insignificant effect on ROCE (β = −0.003, p = 0.313), suggesting that ESG initiatives do not meaningfully influence capital efficiency within the Thai corporate context. The coefficient of ESG is negative and statistically insignificant (β = −0.003, p = 0.313); therefore, Hypothesis H1, which posits a positive effect of ESG on ROCE, is not supported. Similarly, firm size (SIZE) exhibits a negative yet nonsignificant relationship (β = −0.004, p = 0.128), indicating that larger firms do not necessarily achieve superior efficiency in capital utilization. In contrast, leverage (LEVERAGE) demonstrates a highly significant negative association (β = −0.083, p < 0.001), implying that increased debt levels substantially reduce ROCE, with a one-unit rise in leverage corresponding to an estimated 8.32% decline in capital returns. Leverage exhibits a strong and significant negative effect on ROCE (β = −0.083, p < 0.001), thereby supporting Hypothesis H4. The Durbin–Watson statistic of 1.210 suggests no severe autocorrelation, supporting the model’s validity. Collectively, these results underscore the critical influence of financial structure—particularly leverage—on capital efficiency, while highlighting that ESG engagement and firm size play minimal roles in explaining ROCE variation. The low explanatory power further implies that unobserved factors such as industry conditions, managerial efficiency, or macroeconomic trends may better account for differences in capital performance.

4.5.2. Model 2: Impact on Return on Assets (ROA)

Model 2: ROA = β₀ + β₁ESG + β₂SIZE + β₃LEVERAGE + ε

(18)

Model Summary:

R-squared: 0.117 (11.7%)|Adj R-squared: 0.115 (11.5%)

F-statistic: 42.541|Prob(F): 8.163 × 10⁻²⁶ (<0.001)

Durbin–Watson: 1.001

Table 7. Regression Results for Model 2.

Variable	Coefficient	Std Error	t-Statistic	p-Value	Significance
Constant	14.820225	1.114002	13.304	0.000	***
ESG	−0.443536	0.187853	−2.361	0.018	*
SIZE	−0.074853	0.141316	−0.530	0.596
LEVERAGE	−10.454488	1.254311	−8.335	0.000	***

Significance levels: *** p < 0.001, * p < 0.05.

presents the regression results for Model 2, which examines the determinants of Return on Assets (ROA). The model is highly significant (F = 42.541, p < 0.001) and explains 11.7% of the variance in ROA, reflecting greater explanatory power than Model 1, which focused on ROCE. The analysis reveals that Environmental, Social, and Governance (ESG) performance shows a significant negative association with ROA (β = −0.444, p = 0.018), which contradicts the expected positive relationship; hence, Hypothesis H2 is rejected. This finding contradicts Hypothesis H2 and suggests that ESG initiatives in the Thai corporate context may impose short-term financial costs that outweigh immediate profitability gains, consistent with the notion that the benefits of sustainability investments often materialize over a longer horizon. Firm size (SIZE) demonstrates a negative but statistically insignificant relationship with ROA (β = −0.075, p = 0.596), suggesting that larger firms do not necessarily achieve superior returns on assets when leverage and ESG factors are controlled. In contrast, leverage (LEVERAGE) has a pronounced and highly significant negative effect (β = −10.455, p < 0.001), confirming Hypothesis H4 and indicating that greater debt exposure substantially erodes profitability, with a one-unit increase in leverage associated with a 10.45% reduction in ROA. The Durbin–Watson statistic of 1.001 suggests a slight positive autocorrelation, though within an acceptable range. Overall, these results highlight that while leverage remains the dominant determinant of ROA, ESG engagement may entail short-term trade-offs in financial performance, emphasizing the need for firms to balance sustainability objectives with efficient financial management.

Table 8. Comparison between Model 1 (ROCE) and Model 2 (ROA).

Aspect	Model 1 (ROCE)	Model 2 (ROA)	Observation
R2	4.0%	11.7%	Model 2 has better fit
ESG	Not significant	Significant (negative)	Stronger impact on ROA
SIZE	Not significant	Not significant	Stronger impact on ROA
LEVERAGE	−0.083 ***	−10.455 *	Much stronger impact on ROA

Significance levels: *** p < 0.01 and * p < 0.10.

summarizes the comparative analysis between Model 1 (ROCE) and Model 2 (ROA), revealing that the latter demonstrates a markedly superior model fit and explanatory capacity. Model 2 accounts for 11.7% of the variance in ROA, nearly three times greater than the 4.0% explained by Model 1, underscoring its stronger predictive validity. In terms of variable effects, Environmental, Social, and Governance (ESG) performance is statistically significant only in the ROA model, where it shows a negative relationship with profitability, suggesting that ESG investments may have short-term financial trade-offs. Firm size (SIZE) does not exert a significant influence in either model, indicating that organizational scale alone does not explain variations in financial efficiency or asset returns. Leverage (LEVERAGE) consistently demonstrates a negative impact across both models, though its effect is substantially stronger on ROA, highlighting the pronounced sensitivity of asset-based performance to debt levels. Overall, these findings confirm that Model 2 provides a more robust framework for understanding the financial implications of ESG, size, and leverage, with statistical significance levels denoted as *** p < 0.01 and * p < 0.10.

4.6. Summary of Hypothesis Testing

Table 9. Summary of Hypothesis Testing Results.

Hypothesis	Result
H1: ESG performance has a positive effect on ROCE	Not Supported
H2: ESG performance has a positive effect on ROA	Not Supported (Negative effect found)
H3: Firm size has a positive effect on financial performance	Not Supported
H4: Leverage has a negative effect on financial performance	Strongly Supported

presents the summary of hypothesis testing results derived from the regression analyses. The outcomes indicate that Hypotheses H1, H2, and H3 are not supported by empirical evidence, while H4 is strongly supported with high statistical significance (p < 0.001). Specifically, Hypothesis H2, which predicted a positive association between Environmental, Social, and Governance (ESG) performance and Return on Assets (ROA), revealed an unexpected negative effect (β = −0.444, p = 0.018), suggesting that higher ESG engagement may be associated with short-term reductions in profitability. Conversely, Hypothesis H4 is robustly validated, as leverage exhibits a significant negative relationship with both Return on Capital Employed (ROCE) (β = −0.083, p < 0.001) and ROA (β = −10.455, p < 0.001), underscoring the detrimental impact of high debt levels on firm performance. Overall, these findings highlight leverage as the most influential determinant of financial outcomes across both models, while also emphasizing that ESG and firm size do not contribute positively to financial efficiency or profitability in the analyzed context.

4.7. Forecasting Performance of ESG-Augmented Models

To align with the forecasting focus of the journal, we conducted an explicit out-of-sample forecasting exercise to evaluate whether ESG performance contains incremental predictive information for firm financial outcomes. Using a rolling-origin (expanding window) design, one-step-ahead forecasts of return on assets and return on capital employed were generated for 2022–2024. We compared a baseline specification including leverage, firm size, and lagged dependent variables (with year effects to capture common macro shocks) against an ESG-augmented specification that additionally included the ESG score. Forecast accuracy was assessed using RMSE, MAE, and MAPE. The results (

Table 10. Rolling out-of-sample forecast performance by year. Forecast accuracy is evaluated using RMSE, MAE, and MAPE for baseline, ESG-augmented, autoregressive (AR), and ESG–interaction models. Panels A and B report results for ROA and ROCE, respectively.

Target Year	Training Years	Test Year	N	Model	RMSE	MAE	MAPE
Panel A. ROA
2022	2020	2021	193	Baseline	5.794	4.394	425.868
				ESG	5.804	4.352	409.198
				ESG–Interaction	5.827	4.417	461.391
2023	2020–2021	2022	193	Baseline	7.412	5.668	106.482
				ESG	7.481	5.740	104.765
				AR	6.331	4.119	112.588
				ESG–Interaction	7.397	5.645	104.733
2024	2020–2022	2023	192	Baseline	5.841	4.298	163.065
				ESG	5.894	4.359	160.387
				AR	5.244	3.605	194.873
				ESG–Interaction	5.933	4.398	157.678
Panel B. ROCE
2022	2020	2021	193	Baseline	0.102	0.064	379.053
				ESG	0.102	0.065	363.532
				ESG–Interaction	0.102	0.065	444.663
2023	2020–2021	2022	193	Baseline	0.125	0.078	2,041,439.993
				ESG	0.127	0.080	1,368,239.194
				AR	0.114	0.059	19,560,951.760
				ESG–Interaction	0.139	0.096	11,900,097.859
2024	2020–2022	2023	192	Baseline	0.154	0.079	197.336
				ESG	0.155	0.081	195.403
				AR	0.147	0.066	268.625
				ESG–Interaction	0.161	0.089	166.595

and

Table 11. Average out-of-sample forecast accuracy across rolling windows. RMSE, MAE, and MAPE report mean forecast errors for baseline, ESG-augmented, autoregressive (AR), and ESG–interaction models for ROA and ROCE.

Dependent Variable	Model	RMSE	MAE	MAPE	OOS Windows	Total Test N
ROA	Baseline	6.349	4.787	231.805	3	578
	ESG	6.393	4.817	224.784
	AR	5.787	3.862	153.731
	ESG–Interaction	6.385	4.820	241.267
ROCE	Baseline	0.127	0.074	680,672.127	3	578
	ESG	0.128	0.075	456,266.043
	AR	0.131	0.062	9,780,610.192
	ESG–Interaction	0.134	0.083	3,966,903.039

) indicate whether the inclusion of ESG improves out-of-sample predictive performance relative to the baseline model.

Table 10 reports the rolling out-of-sample forecasting performance for return on assets (ROA) and return on capital employed (ROCE) over the 2022–2024 evaluation periods, comparing a baseline specification, an ESG-augmented model, an autoregressive (AR) benchmark, and an ESG–interaction model. Overall, the results indicate that incorporating ESG information does not systematically improve short-term forecast accuracy relative to simpler time-series or financial-structure-based models, and in several cases the AR benchmark provides the most accurate predictions.

For ROA, the AR model yields the lowest forecast errors in both 2023 and 2024, with substantially smaller RMSE and MAE values than the baseline and ESG-based specifications. In 2023, the AR model reduces RMSE by approximately 17% and MAE by nearly 38% relative to the baseline, indicating that profitability persistence dominates the incremental predictive content of contemporaneous ESG scores. A similar pattern is observed in 2024, where the AR model again outperforms the baseline and ESG-augmented models, though the improvement is more moderate. The ESG-augmented model produces only marginal reductions in MAPE relative to the baseline and does not outperform the AR benchmark, suggesting that ESG information adds limited short-horizon predictive power beyond accounting for past profitability and firm fundamentals.

For ROCE, the evidence is even less supportive of ESG-based forecasting gains. In 2023 and 2024, the AR specification again exhibits lower RMSE and MAE than both the baseline and ESG-augmented models, implying that capital efficiency is primarily driven by its own dynamic structure rather than by contemporaneous sustainability scores. The ESG and ESG–interaction models, in contrast, display larger forecast errors and, in some cases, substantially higher MAPE values, reflecting unstable proportional errors and limited robustness in predicting extreme or volatile ROCE realizations. The absence of consistent improvements in ESG-augmented models across years indicates that ESG performance does not serve as a reliable short-term leading indicator of capital efficiency in the Thai market.

Taken together, these rolling out-of-sample results reinforce the view that, during the 2020–2024 period, firm financial performance in Thailand is more accurately predicted by its own historical dynamics and basic financial structure than by ESG scores. While ESG variables may carry long-term strategic and risk-related information, their short-run forecasting contribution appears weak relative to autoregressive benchmarks, particularly in a market characterized by heterogeneous ESG maturity and substantial macroeconomic disturbances associated with the COVID-19 crisis and the subsequent recovery phase.

Table 11 compares the average out-of-sample forecasting accuracy of four model specifications—the baseline financial model, the ESG-augmented model, the autoregressive (AR) benchmark, and the ESG–interaction model—for both return on assets (ROA) and return on capital employed (ROCE) across the three rolling forecast windows. The results indicate that including ESG variables does not lead to a systematic improvement in predictive performance and, in most cases, the simple AR specification provides the most accurate short-horizon forecasts.

For ROA, the AR model yields the lowest mean RMSE and MAE among all specifications, substantially outperforming both the baseline and the ESG-based models. This finding suggests that profitability exhibits strong persistence and that past ROA contains more predictive information for future ROA than contemporaneous ESG scores or financial-structure variables. The ESG-augmented model performs very similarly to the baseline model and does not reduce forecast errors in a meaningful way, while the ESG–interaction model even slightly deteriorates predictive accuracy, indicating that nonlinear or interaction effects of ESG do not enhance short-term forecasting precision.

A similar pattern emerges for ROCE. The AR benchmark again delivers the smallest average forecast errors, whereas the baseline and ESG-augmented models show higher RMSE and MAE values. In addition, the ESG interaction specification produces the weakest performance, with notably larger errors, particularly in terms of MAPE, reflecting instability in predicting proportional changes in capital efficiency. These results imply that, for both measures of financial performance, dynamic autoregressive behavior dominates the short-run predictive content of ESG information.

Overall, the evidence from Table 11 indicates that ESG performance does not provide incremental out-of-sample forecasting power beyond that provided by firms’ historical financial dynamics. While ESG may play an important role in shaping long-term strategic resilience and risk profiles, its contribution to short-horizon prediction of profitability and capital efficiency in the Thai market appears limited, especially when compared with parsimonious time-series benchmarks.

5. Discussion

This study synthesizes empirical findings with existing theoretical and empirical evidence to provide a comprehensive interpretation of how Environmental, Social, and Governance (ESG) performance relates to the financial outcomes of Thai listed companies. The analysis reveals that ESG performance exerts a statistically significant negative effect on Return on Assets (ROA) and no significant effect on Return on Capital Employed (ROCE), suggesting that sustainability initiatives may entail short-term trade-offs in profitability. This outcome contradicts the hypotheses formulated from Stakeholder Theory and the Resource-Based View [22,23], which posit that ESG engagement should enhance financial performance through improved stakeholder relations, reputational gains, and operational efficiency. Instead, the findings align more closely with Agency Theory [26], which argues that ESG activities may sometimes serve managerial interests or incur costs that outweigh immediate benefits. Similar short-term negative associations have been reported in studies of emerging economies, where weak institutional infrastructures, limited investor awareness, and inconsistent ESG disclosure frameworks hinder the financial realization of sustainability investments [2,10].

The relatively low explanatory power of the estimated models (R² = 4.0% for ROCE and R² = 11.7% for ROA) should be interpreted in light of the nature of ESG and firm performance dynamics. ESG-related effects are typically long-term, indirect, and mediated by external institutional and market factors, while the present study focuses on a short panel covering 2020–2024—a period characterized by the COVID-19 shock and post-pandemic adjustment. As a result, contemporaneous panel regressions are unlikely to capture the full economic value of sustainability investments, which tend to materialize through gradual improvements in reputation, risk management, innovation, and stakeholder trust. Hence, the low R² values do not imply the irrelevance of ESG, but rather reflect the difficulty of explaining short-term accounting performance using sustainability indicators whose benefits are predominantly long-term and systemic.

The predominance of leverage as a negative determinant of both ROA and ROCE reinforces established corporate finance theory. The strong inverse relationship between leverage and profitability supports the Trade-Off Theory [51], which posits that excessive debt amplifies financial risk and interest burdens, reducing returns. This result also reflects empirical findings by Dang et al. [19], who demonstrated that firms with high debt ratios in emerging markets face reduced profitability due to elevated financing costs and constrained managerial flexibility. The consistently large magnitude of leverage’s coefficient across both models underscores that capital structure decisions exert a more immediate and measurable impact on firm performance than ESG efforts, particularly in contexts where the financial ecosystem does not yet reward sustainability initiatives.

Firm size, by contrast, shows no significant effect on financial performance, diverging from prior findings that larger firms generally benefit from economies of scale and superior resource access [30]. This insignificance may result from structural inefficiencies or bureaucratic constraints typical of large organizations, as well as the heterogeneous composition of the Thai corporate sector. The diversity of industries within the sample may obscure industry-specific effects where size matters differently—manufacturing and finance may scale benefits differently than services or technology.

Taken together, these results illuminate the contextual complexity of the ESG–performance nexus. In Thailand, where ESG reporting and governance practices are still developing, sustainability investments appear to impose short-term financial costs rather than yield immediate gains. This aligns with Eccles et al. [21], who found that the benefits of high sustainability practices tend to emerge over longer horizons. Furthermore, Granger causality analysis reinforces that ESG predicts ROA but not ROCE, implying that sustainability practices contribute more to asset productivity than to capital efficiency, albeit with delayed or modest returns.

The rejection of three out of four hypotheses, particularly the unexpected negative association between ESG performance and return on assets, indicates that the ESG–financial performance relationship in Thailand differs from patterns commonly reported in developed markets. While prior meta-analyses and studies in Europe and North America document predominantly positive or neutral effects of ESG on profitability [7,15,21], evidence from emerging economies is more mixed, with several studies reporting short-term negative or insignificant impacts [10,11,31].

One important explanation lies in the level of ESG maturity. As shown in

Table A3. Distribution of ESG Scores.

ESG Score	Frequency	Percentage	Cumulative %
1	234	24.2%	24.2%
2	187	19.4%	43.6%
3	289	30.0%	73.6%
4	176	18.2%	91.8%
5	79	8.2%	100.0%
Total	965	100.0%

Note: Sample consists of 965 firm-year observations from Thai listed companies (2020–2024). ESG scores range from 1 (Very Poor) to 5 (Excellent) based on CRISIL methodology.

, more than 43% of the firm-year observations are concentrated in ESG scores of 1 and 2, and only 26.4% achieve scores of 4 or 5. This distribution indicates that most Thai listed firms are still at an early stage of ESG adoption, with sustainability initiatives primarily focused on compliance costs, reporting adjustments, and organizational restructuring rather than on efficiency gains or innovation-driven value creation. In such an early-maturity phase, ESG investments may increase operating costs and capital expenditures without immediately generating productivity improvements, thereby exerting downward pressure on short-term profitability.

Industry structure also plays a role. The Thai stock market is dominated by manufacturing, energy, transportation, and resource-intensive sectors, where environmental upgrades, governance reforms, and social compliance often require substantial upfront investment. Unlike service- or technology-oriented economies, where ESG initiatives can rapidly translate into reputational capital and intangible asset growth, firms in capital-intensive industries may experience a longer gestation period before sustainability strategies contribute positively to financial performance.

These contextual factors help explain why the positive relationships predicted by Stakeholder Theory and the Resource-Based View (H1–H3) are not supported in the short run, while the negative impact of leverage (H4) remains robust and dominant. In an environment where ESG practices are still evolving and financial constraints are binding, capital structure discipline appears to be more critical for performance than sustainability orientation. This interpretation is also consistent with the Granger causality results, which suggest that ESG improvements precede changes in ROA, but with a short-term negative sign, implying that the benefits of ESG may materialize only over a longer horizon once firms move to higher levels of sustainability maturity.

The findings also point to practical and policy implications. For managers, balancing sustainability investments with prudent financial management is essential, as excessive leverage can offset potential ESG benefits. For investors, the results suggest that ESG engagement should be viewed as a long-term strategic commitment rather than a short-term performance enhancer. For policymakers, the evidence underscores the need to strengthen institutional mechanisms—such as ESG-linked financing, tax incentives, and standardized disclosure frameworks—to ensure that responsible corporate behavior is recognized and rewarded in the market.

While this study contributes valuable empirical evidence, several limitations warrant attention. The short observation period (2020–2024) may not capture long-term ESG effects, and the aggregate ESG scores used may mask variation across environmental, social, and governance dimensions. Moreover, potential endogeneity between ESG and financial performance cannot be fully ruled out, suggesting the need for future research employing longitudinal or causal inference approaches [18].

The period 2020–2024 covers the COVID-19 crisis and subsequent recovery, which may have introduced substantial intertemporal variation in firm performance. Descriptive statistics by year show that ROA declined in 2020 and gradually recovered after 2021, while ESG scores exhibited a steady upward trend, reflecting increased sustainability disclosure and regulatory pressure following the pandemic. These dynamics suggest that short-term financial pressures during crisis periods may partially explain the negative contemporaneous association between ESG investment and profitability, as firms faced liquidity constraints while simultaneously strengthening resilience and stakeholder trust.

The out-of-sample forecasting results reported in Table 10 and Table 11 consistently show that the inclusion of ESG variables does not yield systematic improvements in short-term predictive accuracy for either return on assets (ROA) or return on capital employed (ROCE). Across both the average performance measures (Table 11) and the year-by-year rolling evaluation (Table 10), the autoregressive (AR) benchmark generally achieves the lowest forecast errors, indicating that firms’ financial performance is largely driven by its own dynamic persistence. The ESG-augmented and ESG–interaction models perform similarly to, or slightly worse than, the baseline financial specification and do not outperform the AR model on RMSE, MAE, or MAPE. These findings suggest that, within the 2020–2024 period, ESG scores provide limited incremental short-horizon predictive content once past financial performance and basic firm characteristics are taken into account. From a discussion perspective, this evidence supports the view that, in an emerging market with relatively low ESG maturity and substantial macroeconomic turbulence during and after the COVID-19 crisis, sustainability indicators may reflect longer-term strategic positioning rather than act as reliable leading indicators of near-term profitability or capital efficiency.

In summary, this study advances understanding of the ESG–financial performance relationship in an emerging market context by showing that ESG engagement, though essential for long-term sustainability, may initially constrain profitability. By contrast, leverage remains a critical determinant of firm performance, highlighting the primacy of financial structure in explaining short-term outcomes. Comparing these findings with the study’s introduction, which hypothesized a positive ESG–performance link based on global evidence [15,20], reveals a nuanced reality: the ESG–financial performance nexus is not universally positive but highly contingent upon institutional maturity, market structure, and temporal dynamics. Ultimately, the takeaway is that firms in emerging markets must integrate ESG strategically and sustainably—balancing ethical responsibility with financial discipline—to realize its long-term value potential.

6. Conclusions

This study examined the impact of Environmental, Social, and Governance (ESG) performance on the financial outcomes of listed companies in Thailand, revealing a significant negative relationship between ESG and Return on Assets (ROA) and no significant association with Return on Capital Employed (ROCE). Leverage emerged as the most influential determinant of financial performance, exerting a strong negative effect on both indicators, while firm size showed no significant impact. These results contribute to the ESG–finance literature by providing empirical evidence from an emerging market context, where institutional frameworks, investor awareness, and market incentives for sustainability remain underdeveloped. The study addresses existing research gaps by integrating causality testing and robust panel regression, while acknowledging limitations related to the short observation period, potential endogeneity, and reliance on aggregated ESG scores. These limitations present opportunities for future research, which could employ longitudinal or disaggregated analyses to uncover long-term and pillar-specific effects. Overall, the findings underscore that while ESG initiatives may not yield immediate financial benefits, they play a crucial role in shaping firms’ long-term resilience and social legitimacy, emphasizing the need for policymakers, investors, and managers to balance financial efficiency with sustainable value creation.

From a policy perspective, the findings suggest an important role for Thai capital-market regulators, particularly the Stock Exchange of Thailand (SET) and the Securities and Exchange Commission of Thailand (SEC), in accelerating ESG maturity. Targeted incentives such as ESG-linked listing privileges, preferential financing schemes, or tax benefits, together with more standardized and mandatory ESG disclosure aligned with ISSB/IFRS S1–S2, could help reduce information asymmetry, lower compliance costs, and enable firms to internalize the long-term financial benefits of sustainability investments.

This study is subject to several limitations that should be considered when interpreting the results and assessing their generalizability. First, the sample period (2020–2024) coincides with the COVID-19 crisis and the early post-pandemic recovery, which may have temporarily distorted both financial performance and ESG investment behavior, thereby affecting the estimated short-term relationships. Second, the analysis relies on an aggregated ESG index, which may mask heterogeneous effects of the environmental, social, and governance pillars and may introduce multicollinearity-related attenuation. Third, although panel regression and Granger causality tests provide insights into dynamic predictability, they do not establish structural causation and may still be affected by endogeneity and omitted-variable bias. Finally, the findings are based on Thai-listed firms operating in an emerging market with relatively low ESG maturity and a high concentration of capital-intensive industries; therefore, the results may not be directly generalizable to developed economies or markets with more advanced sustainability frameworks. Future research using longer time horizons, disaggregated ESG measures, and causal identification strategies (e.g., dynamic GMM or natural experiments) would help to validate and extend the conclusions.

In contrast to much of the existing literature that focuses primarily on contemporaneous correlations between ESG scores and firm performance in developed markets, this study provides new evidence on the dynamic and predictive relationship between ESG performance and accounting-based financial indicators in an emerging-market context. By integrating panel econometric modeling, Granger causality analysis, and out-of-sample forecast evaluation, the study demonstrates that ESG performance exhibits limited and delayed short-term predictive power for profitability, whereas financial performance is largely driven by its own dynamic persistence. Moreover, the analysis highlights the role of ESG maturity and industry structure in shaping the sustainability–performance nexus in Thailand. These contributions extend prior ESG–finance research by adopting a forecasting-oriented perspective and by providing time-series evidence from an ASEAN capital market that remains underrepresented in the literature.