Integrating Environmental, Social, and Governance (ESG) Factors into the Investment Returns of American Companies

Abstract

This study investigates the influence of Environmental, Social, and Governance (ESG) factors on the financial performance of publicly traded U.S. companies between 2013 and 2023. Using a balanced panel dataset of 386 S&P 500 firms and 4246 firm-year observations, the analysis applies panel data regression models with fixed effects to evaluate the association between ESG scores and two financial indicators: Return on Assets (ROA) and Tobin’s Q. The results reveal a modest association with ROA, but a significantly stronger link with Tobin’s Q, suggesting that while ESG practices may not substantially boost short-term profitability, they are positively perceived by investors and contribute to long-term market value. These findings are consistent with stakeholder and signalling theories, indicating that strong ESG performance reflects effective management and lower investment risk. The limited impact on ROA may stem from the initial costs of implementing ESG initiatives. This study highlights practical implications for corporate leaders and policy-makers, advocating for ESG integration as a long-term value driver. Future research should explore alternative ESG rating systems and consider sectoral dynamics and broader market influences.

1. Introduction

In recent years, Environmental, Social, and Governance (ESG) considerations have become central to corporate strategy and investment decision-making, reflecting a global shift toward sustainability and responsible business conduct [1,2]. ESG provides a structured framework for assessing a firm’s impact on environmental systems, societal wellbeing, and governance practices, and has evolved into a core evaluative tool for investors seeking long-term value [3]. Empirical studies increasingly suggest that strong ESG profiles are associated with improved financial performance, reduced risk exposure, and enhanced stakeholder trust [4,5].

Despite growing interest, the financial consequences of ESG integration remain subject to debate. While some evidence indicates positive returns linked to ESG engagement, other studies find more nuanced or inconclusive outcomes. In particular, the distinction between short-term profitability and long-term market valuation continues to raise important questions for both researchers and practitioners. As ESG metrics become more influential in shaping investor behaviour [6], understanding how ESG integration translates into financial performance is a pressing concern.

While global trends highlight the growing importance of sustainability, the United States represents a particularly distinctive context. ESG disclosure in the U.S. remains largely voluntary and heterogeneous, resulting in significant variation across firms and industries. At the same time, the policy landscape is evolving, as reflected in the U.S. Securities and Exchange Commission’s (SEC) proposed climate-related disclosure rules, which seek to standardise and enhance ESG transparency [7]. These developments make the U.S. market a critical setting for examining whether ESG engagement translates into measurable financial benefits, particularly given the ongoing debate about its relevance for both investors and managers.

Accordingly, this study addresses the following research question: Does ESG performance significantly influence the financial performance of U.S. publicly traded firms, as measured by Return on Assets (ROA) and Tobin’s Q?

To answer this question, this study uses a balanced panel dataset of 386 S&P 500 firms over the period 2013–2023 and applies panel data regression models with fixed effects to evaluate the impact of ESG scores on financial performance. Drawing on panel data regression models, the analysis evaluates whether ESG scores sourced from Refinitiv Eiko are associated with Return on Assets (ROA) and Tobin’s Q, representing accounting- and market-based measures of firm performance, respectively. By leveraging Bloomberg financial data and controlling for firm size, leverage, and industry-specific characteristics, this study aims to isolate the effect of ESG engagement on firm value.

The contribution of this study is threefold. First, it provides updated evidence on the ESG performance nexus using a large sample across a full decade, encompassing a period of increasing ESG relevance in capital markets. Second, it evaluates both short-term (ROA) and long-term (Tobin’s Q) performance effects, offering a better understanding of ESG’s financial impact. Third, it contributes to the growing body of empirical ESG literature by focusing on the U.S. context, where ESG disclosure remains largely voluntary and heterogeneous, thus offering insights into how ESG performance matters in a market characterised by evolving, yet non-uniform, standards. In doing so, this study informs corporate decision-makers and policy-makers seeking to understand whether and how ESG strategies contribute to financial value creation.

The remainder of the paper is structured as follows: Section 2 outlines the theoretical framework and hypotheses development. Section 3 details the data and methodology. Section 4 presents the empirical results and discussion. Section 5 concludes with the theoretical and policy implications, along with the limitations, of this study.

2. Theoretical Framework and Hypotheses Development

2.1. Understanding ESG and the Role of Disclosure Standards

Environmental, Social, and Governance (ESG) represents a structured set of disclosure standards designed to evaluate a firm’s non-financial performance across three dimensions: environmental impact, social responsibility, and governance practices. These standards provide intangible but critical information that goes beyond conventional financial measures [3,8]. While much of the existing literature has disproportionately focused on the environmental pillar, this approach contrasts with the investment community’s practice of evaluating firm risk and performance based on a holistic view of all ESG dimensions [9]. A clear understanding of the criteria underlying each ESG pillar is therefore essential.

The environmental component assesses a firm’s interaction with the natural environment, considering factors such as greenhouse gas emissions, water usage, and waste management [10]. The social dimension evaluates a firm’s relationships with employees, suppliers, customers, and communities, including matters related to workforce diversity and human rights [11]. The governance pillar concerns the internal governance structure, covering executive compensation, audit processes, internal controls, and shareholder rights protection [12]. Together, these elements form the basis of ESG data used by investors and analysts to evaluate corporate sustainability and ethical conduct.

The rise in ESG relevance has led companies to increasingly disclose a broad range of non-financial metrics [13], resulting in a global proliferation of voluntary ESG disclosures [14,15]. However, the credibility and comparability of self-reported ESG data remain contentious. Various ESG rating agencies—such as MSCI, Sustainalytics, and S&P Global—have developed proprietary methodologies to assess firm performance across ESG criteria [16]. Yet, significant variation in these methodologies introduces inconsistencies and interpretive challenges [17].

Despite these concerns, ESG scores continue to serve as a vital resource for investors seeking objective evaluations of a firm’s sustainability efforts and their potential impact on financial performance. As Amel-Zadeh and Serafeim [18] note, while ESG scores are not without limitations, they offer a practical tool for assessing corporate environmental and social behaviour. In this study, ESG data are sourced from Refinitiv, a prominent and widely adopted ESG rating provider [19].

Although the United States does not yet mandate comprehensive ESG disclosure, recent regulatory developments signal a shift toward standardisation. The U.S. Securities and Exchange Commission (SEC) has proposed new climate-related disclosure rules as of 2022, reflecting growing regulatory interest in enhancing ESG [7]. We view these initiatives as an important step toward reducing inconsistencies in ESG reporting, which have long hindered both academic research and investor decision-making. Prior studies have highlighted the weaknesses of self-reported ESG information, stressing the need for harmonised and credible reporting frameworks [14,17]. In our opinion, while self-reported disclosures have contributed to raising awareness, their fragmented nature often limits comparability and reduces the reliability of ESG metrics. As Matos [20] argues, improved standardisation would enhance the usefulness of ESG information for evaluating long-term risk and firm value. We agree with this assessment and suggest that the forthcoming regulatory changes in the U.S. may narrow the gap between voluntary disclosure practices and the more consistent reporting frameworks already emerging in Europe. This alignment has the potential not only to improve research quality but also to strengthen investor confidence in ESG scores as a genuine reflection of corporate sustainability performance.

2.2. Measures of Financial Performance

The relationship between ESG performance and financial profitability has been extensively examined in the literature through a variety of financial metrics. Ferrell et al. (2016) [21] adopt a comprehensive approach, employing Tobin’s Q, Return on Assets (ROA), and Return on Equity (ROE) to assess the financial consequences of socially responsible corporate behaviour. Their findings underscore the importance of applying multiple performance indicators to capture both market-based and accounting-based dimensions of financial outcomes.

Further, Drempetic et al. [22] explore the influence of firm size on ESG scores, emphasising the necessity of incorporating size-related controls when analysing the relationship between ESG indicators and financial performance. Their research relies on variables such as market capitalisation and total assets to account for firm heterogeneity in ESG impact assessments. Similarly, Aouadi and Marsat [23] investigate the effect of ESG controversies on firm valuation, employing Tobin’s Q as the principal measure of market perception. Their study controls for firm size, profitability (ROA), and financial leverage, thereby offering a nuanced view of how both positive ESG practices and reputational risks influence financial outcomes across different sectors and institutional settings.

In line with these prior studies, the present research utilises two widely recognised financial metrics: Return on Assets (ROA), as a proxy for operational efficiency and internal profitability, and Tobin’s Q, as an indicator of market valuation relative to the replacement cost of assets. Together, these metrics enable a comprehensive assessment of how ESG engagement relates to both short-term financial performance and long-term value creation.

2.3. Empirical Evidence on the ESG–Financial Performance Relationship

The relationship between Environmental, Social, and Governance (ESG) performance and corporate financial outcomes has been extensively investigated in academic literature, yet findings remain inconclusive and, at times, contradictory. A substantial body of research documents a positive relationship, indicating that firms with stronger ESG engagement tend to outperform their peers in terms of profitability and market valuation. Maji and Lohia [24] suggest that superior ESG performance correlates with elevated equity valuations, while Parfitt [25] argues that ESG integration mitigates environmental, social, and governance risks, contributing to both shareholder value and societal benefit. Similarly, Katelouzou and Klettner [26] and Pacelli et al. [5] assert that ESG considerations enhance long-term value creation, with Foltynowicz and Kaps [27] framing this within the broader transition to sustainable corporate accountability.

Numerous empirical studies reinforce this positive association. Velte [28] finds that ESG practices improve Return on Assets (ROA) in German firms, particularly through effective governance mechanisms. In Korea, Yoon et al. [29] demonstrate a significant positive influence of corporate social responsibility on firm valuation. Comparable findings emerge from studies in China’s energy sector [30], India [31], and other global markets. Xie et al. [32] and Bhaskaran et al. [33] observe that firms with strong ESG performance report higher Tobin’s Q, ROA, and ROE, reflecting market confidence in their long-term prospects. Aydoğmuş et al. [34] further confirm the positive valuation effect, suggesting that robust ESG practices are perceived as signals of lower risk and greater resilience [1,35,36,37]. De Lucia, Pazienza, and Bartlett [38], in their study of over 1000 European firms, also find that higher ESG performance corresponds with improved ROE and ROA.

Evidence from the UK context is also supportive. Li et al. [39] find that higher ESG disclosure among 367 FTSE-listed firms is associated with increased firm value, driven by improved stakeholder confidence. Similarly, Ahmad et al. [40] report that overall ESG scores positively influence financial performance among FTSE 350 firms between 2002 and 2018, despite some variability in the effects of individual ESG dimensions. In a sector-specific study, Abdi et al. [41] show that ESG engagement improves financial and market performance in the airline industry, with social and environmental initiatives linked to higher profitability, and governance efforts associated with improved market-to-book ratios.

Evidence on the ESG–financial performance relationship is not uniformly positive, and many studies highlight its context-dependent nature. Several scholars report a negative association between ESG activity and firm value, particularly where ESG initiatives are perceived as costly or misaligned with market expectations. Palupi [42] finds that ESG performance is negatively related to firm value in ASEAN markets, as environmental initiatives and nonfinancial disclosures are seen as burdensome and reduce investor confidence. Similarly, Auer and Schuhmacher [43] show that while ESG-based investments generally perform in line with the market, in Europe they may underperform, indicating that socially responsible investing can entail a performance cost and even a negative relationship with firm value. Landi and Sciarelli [44] report a negative correlation between ESG scores and financial performance in Italian firms, while Folger-Laronde et al. [45] demonstrate that strong ESG performance did not shield Canadian ETFs from losses during the COVID-19 pandemic. Nollet et al. [46] also find a negative relationship between financial and social performance among S&P 500 firms, though this varies by model specification. Studies from Latin America [47] and other emerging economies [48] similarly show that ESG investments can harm profitability when they are not aligned with industry-specific priorities.

Other studies report no significant relationship, further reinforcing the view that ESG effects depend on context. Domanović [49] finds no direct link between ESG engagement and financial outcomes in Serbian energy firms, while Plumlee et al. [50] report no association between voluntary ESG disclosure and firm value, cash flows, or investment costs. Lopez-de-Silanes et al. [51], in a multi-country study, similarly conclude that ESG scores do not significantly impact financial performance.

Adding to this complexity, some research shows mixed effects across ESG pillars. Han et al. [52] find that governance scores are positively associated with profitability in Korean firms, while environmental scores show a negative association and social scores no effect. Atan et al. [53] report no significant effect of ESG factors on profitability or firm value in Malaysian firms, and Saygili et al. [54] find that governance and social factors positively influence performance among Turkish firms, whereas environmental disclosure has a negative impact. Giannopoulos et al. [35] note a weak link between ESG and profitability but a strong positive association with Tobin’s Q among Norwegian firms. Behl et al. [55] similarly report ambiguous findings in the Indian energy sector. Collectively, these studies reflect the methodological and contextual complexity of evaluating ESG-financial performance linkages. Key limitations across this literature include inconsistent ESG measurements across rating agencies and the potential lagged effects of ESG investments, which may not be captured within shorter study horizons. As such, the mixed findings underscore the importance of considering industry dynamics, regional variations, and time horizons when assessing the financial implications of ESG engagement. Future research should aim to address these challenges by enhancing methodological standardisation and exploring the underlying mechanisms that condition the ESG–financial performance relationship.

2.4. Hypothesis Development

The wide-ranging and often contradictory empirical findings discussed above reflect the multifaceted nature of the relationship between ESG performance and financial outcomes. These mixed results suggest that any observed financial effects of ESG engagement are likely mediated by contextual factors, temporal dimensions, and underlying strategic intent. To better interpret these variations, it is essential to examine the relationship through established theoretical lenses that offer explanatory depth.

Several theoretical perspectives have been employed to frame the ESG performance nexus, and more recent research continues to affirm their relevance. Stakeholder theory posits that by addressing the expectations of a broad range of stakeholders, firms can secure long-term sustainability and legitimacy, thereby enhancing financial performance [56]. These stakeholders may include customers, employees, the media, and local communities [57], who exert influence by shaping revenue streams, constraining access to resources, and affecting corporate reputation [58]. Empirical studies applying stakeholder theory to the ESG–financial performance relationship include [59], Sassen et al. [60], and Benlemlih et al. [61]. In parallel, the resource-based view (RBV) suggests that ESG practices function as intangible assets such as reputation, employee engagement, or innovative capacity that provide competitive advantage and differentiation [62]. This perspective has been widely adopted in recent studies [63,64,65], underscoring its continued applicability in sustainability research. Finally, signalling theory argues that strong ESG performance conveys credible signals to investors and other external stakeholders about the firm’s operational integrity, managerial competence, and reduced exposure to long-term risks [66]. Contemporary applications of signalling theory in the ESG domain include Friske et al. [67], Lee et al. [68], and Quintiliani [69], which collectively demonstrate the enduring relevance of this theoretical framework.

Together, these theoretical frameworks provide valuable insight into why ESG practices might be associated with improved financial outcomes, and why such associations may vary across firms and contexts. They also help explain why firms may voluntarily engage in ESG reporting and implementation, even in the absence of mandatory regulation or immediate profitability gains.

Given the extensive yet inconsistent empirical findings outlined previously, and informed by stakeholder theory, RBV, and signalling theory, the following hypothesis is proposed:

H1: 

There is a significant positive relationship between ESG considerations and the financial performance of U.S. publicly traded corporations.

This hypothesis acknowledges the complex, multidimensional character of ESG engagement and its potential financial implications. It also provides a foundation for further empirical investigation, particularly into the mechanisms through which ESG factors influence performance and how these mechanisms may differ by industry, firm characteristics, or governance structures.

3. Research Design

3.1. Data and Sample

The sample for this study comprises companies listed on the S&P 500 index. A judgmental sampling technique was employed to identify the top 500 firms by market capitalisation using the Bloomberg Terminal, with an inclusion criterion of a minimum market value of USD 2.85 billion during the period 2013 to 2023. This initial screening yielded 5500 firm-year observations. Financial institutions were subsequently excluded from the dataset, as certain ESG components, particularly those related to environmental disclosures, are less applicable to the operational characteristics of firms in the financial sector. Following this exclusion, the sample was reduced to 472 firms.

To ensure consistent ESG data availability, the sample was further refined by retaining only those firms with complete ESG scores available in the Refinitiv Eikon database. This resulted in a final balanced panel of 386 firms and 4246 firm-year observations. The dependent variables—Return on Assets (ROA) and Tobin’s Q—as well as the control variables (firm size, liquidity, financial leverage, board size, board diversity, and board independence) were obtained from the Bloomberg Terminal. The key independent variable, the combined ESG score, was sourced from Refinitiv Eikon.

The data were collected on an annual basis and transformed into a panel data structure to facilitate longitudinal analysis. The combined use of Bloomberg and Refinitiv Eikon, two widely recognised and reliable financial databases, ensures the robustness, consistency, and credibility of the dataset. This methodological approach allows for a comprehensive examination of the relationship between ESG performance and financial outcomes among major U.S. corporations over a significant ten-year horizon. An overview of the sample selection process is provided in

Table 1. Data Sample.

Stage of Selection	Initial S&P 500 Companies	Companies After Exclusion	Companies with Available ESG Scores
Number of Companies	500	472	386
Total Firm-Year Observations	5500	5192	4246

.

3.2. Variables Measurement

3.2.1. Dependant Variables

Return on Assets (ROA) is widely recognised as a reliable indicator of operational efficiency, capturing how effectively management utilises corporate resources to generate profits [70]. By expressing net income as a proportion of total assets, ROA provides a clear and comparable measure of profitability across firms of varying sizes and industries [71]. In contrast, Tobin’s Q serves as a market-based metric that evaluates firm value and performance by comparing a company’s market valuation to the replacement cost of its assets [72]. This ratio not only reflects current financial performance but also captures investor expectations regarding future growth and managerial quality [73]. The concurrent use of accounting-based indicators such as ROA and market-based measures like Tobin’s Q enables a more comprehensive assessment of firm performance, as supported by several scholars (e.g., [74,75]). This dual approach allows for a robust evaluation of corporate performance from both internal operational and external market perspectives, thereby enhancing the reliability and generalizability of the study’s conclusions [76].

Tobin’s Q is calculated as the ratio of the market value of assets to the book value of assets. The market value of assets is computed as the market value of equity (share price × number of outstanding shares) plus the market value of liabilities. Consistent with prior research, we approximate the market value of liabilities using the book value of total liabilities, which provides a reliable proxy in the absence of market quotations for debt. This approach is widely used in corporate finance and ESG research, allowing for comparability with earlier studies [77,78].

3.2.2. Independent Variable: ESG Performance Score

The primary independent variable in this study is the combined Environmental, Social, and Governance (ESG) score. A significant proportion of scholars in the field have adopted ESG scores from Refinitiv Eikon as a reliable and objective measure for examining sustainability-related corporate performance [79,80,81]. In alignment with these established practices, this study employs the Refinitiv ESG score, which is widely recognised for its credibility and extensive use in academic research [60].

Refinitiv applies a rigorous data collection methodology that draws from a broad spectrum of global corporate disclosures (Environmental, Social and Governance Scores) from LSEG. The ESG score is structured around three core pillars: Environmental, which assesses factors such as resource use, carbon emissions, and innovation in environmental technologies; Social, which covers labour standards, ethical practices, community engagement, and customer health and safety; and Governance, which evaluates corporate structure, shareholder rights, and the implementation of corporate social responsibility strategies. These pillars are further disaggregated into distinct themes, offering a comprehensive and multidimensional evaluation of a firm’s ESG performance [21].

The Refinitiv scoring system ranges from 0 (D–) to 100 (A+), where a higher score denotes stronger ESG performance. This granular scale facilitates nuanced analyses of ESG practices and their relationship with key corporate outcomes. By adopting this externally validated and standardised measure, the study ensures objectivity in evaluating the impact of ESG performance on firm-level financial indicators.

3.2.3. Control Variables

The inclusion of control variables is essential in ESG–financial performance research, as it allows for the isolation of the independent effect of ESG scores while accounting for firm-specific characteristics that may confound the relationship [82]. This study incorporates six control variables commonly adopted in the literature to improve model robustness and comparability.

1-

Firm Size: Measured by the natural logarithm of total assets, firm size is included as larger firms often exhibit higher ESG scores due to increased visibility, stakeholder pressure, and resource availability. Additionally, firm size may influence financial performance directly through economies of scale and market power [22,83].

2-

Liquidity: Represented by the current ratio (current assets divided by current liabilities), liquidity reflects a firm’s short-term financial health and operational flexibility. Higher liquidity can enhance a firm’s capacity to implement ESG initiatives and absorb associated costs [84,85].

3-

Financial Leverage: Calculated as the ratio of total debt to total equity, leverage captures the degree of financial risk borne by the firm. Highly leveraged firms may face constraints in pursuing ESG objectives or may adopt them strategically to reduce perceived risk [79].

4-

Board Size: Defined as the total number of directors on the board, board size is considered a proxy for governance capacity and decision-making dynamics. Larger boards may offer greater diversity of opinion but could also reduce efficiency in overseeing ESG strategies [86,87].

5-

Board Independence: Measured as the proportion of independent (non-executive) directors, this variable captures the board’s capacity to monitor management objectively and uphold stakeholder interests, which is critical for the implementation and oversight of ESG policies [88,89].

6-

Board Diversity: This variable reflects the heterogeneity in board members’ backgrounds and experiences. A diverse board is often linked to enhanced decision-making and a stronger emphasis on sustainability and ethical practices, thus contributing positively to ESG performance [90,91].

Board Structure is operationalised in our model using three distinct board-level variables: Board Size (total number of directors on the board), Board Independence (percentage of independent directors on the board), and Board Diversity (percentage of female directors). These variables are included to capture key governance mechanisms that can influence firm decision-making and performance. Together, they provide a comprehensive view of the board’s composition and functioning, which has been shown to affect monitoring quality and strategic outcomes.

These control variables are included to ensure the validity of the empirical models and to reduce omitted variable bias. A summary of all variables, including definitions and data sources, is presented in

Table 2. List of Variables.

	Description/Formula	Source
Dependent variables
ROA- Return on Asset	Net Income/Total Assets	Bloomberg
Tobin’s Q	Equity Market Value + Liabilities Market Value)/(Equity Book Value + Liabilities Book Value)	Bloomberg
Independent variable
ESG Score	Combined ESG Disclosure Score	Refinitiv
Control variables
Log Total Asset	Logarithm of total assets	Bloomberg
Current Ratio	Current Assets/Current Liabilities	Bloomberg
Financial Leverage	Total Debt/Total Equity	Bloomberg
Board Size	The total number of directors	Bloomberg
Board Independence	Percentage of independent directors	Bloomberg
Board Diversity	Percentage of women on board	Bloomberg

.

3.2.4. Regression Model

To examine the relationship between Environmental, Social, and Governance (ESG) factors and the financial performance of U.S. publicly traded companies, this study employs panel data regression analysis—a widely accepted econometric approach for evaluating firm-level sustainability and financial dynamics. Regression analysis enables the investigation of the strength, direction, and significance of the association between ESG scores and firm performance, offering quantitative insights into how variations in ESG practices may affect profitability and market valuation. To explore the possibility that different ESG dimensions may have heterogeneous effects, we also decomposed the ESG score into its E, S, and G components and re-estimated our models.

The panel data methodology is particularly suited for this analysis, as it captures both cross-sectional and time-series variations by observing multiple firms over a ten-year period (2013–2023). This dual dimension allows for the control of unobserved heterogeneity across firms and temporal fluctuations, thereby enhancing the robustness and validity of the empirical results [92,93]. By incorporating firm-specific and time-invariant characteristics, panel data models offer a more comprehensive view of how ESG performance contributes to financial outcomes over time.

This study employs STATA 19 for regression analysis, due to its proven effectiveness in managing large, complex datasets and conducting panel data estimations [94]. Stata’s built-in capabilities for fixed and random effects models are particularly well-suited for analysing firm-level panel data [95]. It also provides essential diagnostic tools for evaluating model accuracy and data fit [96], along with efficient data handling features that support reliable empirical analysis [97].

This study utilises a linear panel regression model, with two dependent variables, Return on Assets (ROA) and Tobin’s Q, serving as proxies for internal profitability and market valuation, respectively. The main explanatory variable is the composite ESG score obtained from Refinitiv Eikon. To control for firm-level heterogeneity and potential confounding effects, the model includes a set of control variables: firm size (measured by total assets), financial leverage (debt-to-equity ratio), liquidity (current ratio), and governance-related characteristics (board size, board diversity, and board independence).

The panel regression models are specified as follows:

ROA_it = β₀ + β₁ESG_it + β₂SIZE_it + β₃LEV_it + β₄BOARD STRUCTURE _it + ɛ

Tq_it = β₀ + β₁ESG_it + β₂SIZE_it + β₃LEV_it + β₄BOARD STRUCTURE _it + ɛ

where

• The market valuation and return on assets for firm I in period t is measured by the dependent variables, Tobin’s Q and ROA.
• The independent variable, ESG, represents the ESG scores assigned to firm i during the same period.
• Control variables encompass firm size (SIZE), financial leverage (LEV), and board structure (BOARD STRUCTURE) for firm i in period t.
• The error term is denoted by ε.

Board Size, Board Independence, and Board Diversity are included as additional controls to capture specific board-level governance mechanisms. Although the Governance (G) pillar of the ESG score aggregates multiple governance dimensions, it does not allow us to observe the independent effect of board composition characteristics. By including these controls, we can disentangle board-level governance from the broader governance composite, thereby reducing potential omitted variable bias.

These models aim to isolate the effect of ESG engagement on firm financial performance by controlling for internal firm characteristics that may also influence profitability or valuation. By employing a robust panel regression framework, the study contributes empirical evidence on the materiality of ESG practices within the U.S. corporate context.

4. Empirical Results and Discussion

4.1. Descriptive Statistics

Table 3. Descriptive Statistics.

	Mean	Median	Std. Dev.	Min	Max
Dependent Variables
Return on Asset	7.46	6.68	7.764	−61.821	59.248
Tobin’s Q	2.786	2.09	2.158	0.627	23.563
Independent Variable
ESG Score	50.965	51.83	13.041	5.09	85.806
Control Variables
Log Total Asset	9.72	9.75	1.307	4.665	13.395
Current Ratio	1.826	1.44	1.444	0.136	19.266
Financial Leverage	10.29	2.71	159.107	1.061	9656.5
Board Size	10.681	11.00	1.98	3	18
Board Independence	84.165	88.89	9.641	27.273	100
Board Diversity	24.38	25.00	10.863	0	70

presents the descriptive statistics for the variables employed in the analysis. Firm financial performance is assessed using two key indicators: Return on Assets (ROA) and Tobin’s Q. ROA, which captures profitability, reports a mean of 7.46% and a median of 6.68%. Tobin’s Q, a market-based valuation metric, has a mean of 2.786 and a median of 2.09. The fact that Tobin’s Q exceeds 1 on average suggests that a significant number of firms in the sample may be overvalued by the market.

The main independent variable, the ESG score, has a mean of 50.965 and a median of 51.83, indicating a moderate level of ESG engagement across the sampled firms. Among the control variables, firm size—measured as the natural logarithm of total assets—has a mean of 9.72 and a median of 9.75. The current ratio, representing short-term liquidity, averages 1.826 with a median of 1.44. Financial leverage, calculated as the total debt-to-equity ratio, exhibits considerable dispersion, with a mean of 10.29 and a median of 2.71. This reflects the presence of outliers with exceptionally high leverage levels.

Corporate governance variables show that the average board size is approximately 11 members. Board independence is notably high, with a mean of 84.165%, while board gender diversity—measured by the proportion of women directors—averages 24.38%. Standard deviations for all variables fall within expected ranges, and the minimum and maximum values are reported in the final rows of Table 3.

4.2. Pearson Correlation Matrix

Table 4. Pearson Correlation Matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
(1) ROA	1
(2) Tobin’s Q	0.406 ***	1
(3) ESG Score	−0.036 **	−0.167 ***	1
(4) Log Total Asset	−0.158 ***	−0.414 ***	0.533 ***	1
(5) Current Ratio	0.203 ***	0.241 ***	−0.237 ***	−0.302 ***	1
(6) Financial Leverage	−0.007	0.007	0.018	0.010	−0.026 *	1
(7) Board Size	−0.079 ***	−0.212 ***	0.372 ***	0.499 ***	−0.240 ***	−0.008	1
(8) Board Independence	−0.024	−0.107 ***	0.304 ***	0.183 ***	−0.105 ***	−0.005	0.147 ***	1
(9) Board Diversity	0.054 ***	0.034 **	0.427 ***	0.272 ***	−0.143 ***	0.015	0.128 ***	0.221 ***	1

*** p < 0.01, ** p < 0.05, * p < 0.1.

presents the Pearson correlation matrix, revealing several noteworthy associations among the variables. Return on Assets (ROA) and Tobin’s Q are moderately positively correlated (r = 0.406), suggesting that higher internal profitability is associated with stronger market valuations. The ESG score does not exhibit a significant correlation with ROA, but it shows a weak negative correlation with Tobin’s Q (r = −0.167), implying that higher ESG performance may not necessarily align with investor valuation.

A moderate positive correlation is observed between ESG score and firm size, measured as the logarithm of total assets (r = 0.533), indicating that larger firms are more likely to engage in comprehensive ESG disclosures. This pattern is consistent with positive correlations between firm size and governance-related attributes, including board size, board independence, and board diversity.

Conversely, firm size demonstrates negative correlations with both ROA (r = −0.158) and Tobin’s Q (r = −0.414), suggesting that larger firms may experience relatively lower profitability and market valuation. The current ratio shows weak positive associations with ROA and Tobin’s Q, indicating a modest link between liquidity and financial performance. Financial leverage does not display significant correlations with any of the other variables, highlighting its relative independence within the dataset.

According to

Table 5. Variance Inflation Factor.

	VIF
Log Total Asset	1.917
ESG Score	1.682
Board Size	1.369
Tobin’s Q	1.279
Board Diversity	1.272
Current Ratio	1.156
Board Independence	1.123
Financial Leverage	1.002
Mean VIF	1.35

(VIF), the correlation is under the threshold, which means that multicollinearity is not a concern. The results show no VIF exceeds3, suggesting that multicollinearity is not an issue for the analyses. If VIF is higher than 10, major multicollinearity may take place [98].

4.3. Hausman Test

In our panel data analysis, we employed the Hausman test to determine the appropriate model specification between fixed effects and random effects.

Table 6. Hausman Test Result.

Dependent Variable	Test Result	p-Value	Chi-Square Statistic
ROA	Fixed effects	0.000	49.22
Tobin’s Q	Fixed effects	0.000	89.472

presents the results for the two dependent variables, Return on Assets (ROA) and Tobin’s Q, both yielding highly significant p-values of 0.000, well below the 5% significance level. Consequently, we reject the null hypothesis in favour of the fixed effects model over the random effects alternative. This outcome indicates the presence of unobserved, time-invariant heterogeneity across entities that correlates with the explanatory variables. Employing the fixed effects model thus enables us to control for these entity-specific effects, resulting in more robust and consistent parameter estimates. Furthermore, the larger chi-square statistic observed for Tobin’s Q (89.472), relative to ROA (49.22), underscores an even stronger justification for applying the fixed effects specification when Tobin’s Q serves as the dependent variable.

4.4. Regression Results

The regression results, including coefficients, t-values, and significance levels, are summarised in

Table 7. Fixed Effects (GLS): ROA.

Return on Asset	Coefficient	Std. Error	t-Value	p-Value
Constant	−2.969	2.864	−1.04	0.3
ESG Score	0.028	0.016	1.70	0.089 *
Log Total Asset	0.718	0.288	2.49	0.013 **
Current Ratio	0.38	0.128	2.96	0.003 ***
Financial Leverage	0	0.001	−0.21	0.832
Board Size	−0.066	0.087	−0.76	0.45
Board Independence	0.012	0.019	0.60	0.548
Board Diversity	0.042	0.015	2.91	0.004 ***
Mean dependent var 7.432		SD dependent var 7.683
R-squared 0.017		Number of Obs. 4127
F-test 9.057		Prob > F 0.000
Akaike crit. (AIC) 25,769.675		Bayesian crit. (BIC) 25,820.277

*** p < 0.01, ** p < 0.05, * p < 0.1.

,

Table 8. Fixed Effects (GLS): ROA by ESG Pillar.

Return on Asset	Coefficient	Std. Error	t-Value	p-Value
Constant	−2.851	2.905	−0.98	0.326
Environmental (E)	0.006	0.013	0.48	0.632
Social (S)	0.025	0.015	1.67	0.096 *
Governance (G)	0.031	0.016	1.93	0.054 *
Log Total Asset	0.703	0.289	2.43	0.015 **
Current Ratio	0.365	0.129	2.82	0.005 ***
Financial Leverage	−0.000	0.001	−0.24	0.809
Board Size	−0.063	0.088	−0.72	0.470
Board Independence	0.013	0.019	0.66	0.509
Board Diversity	0.039	0.015	2.66	0.008 ***
Mean dependent var 7.432		SD dependent var 7.683
R-squared 0.22		Number of Obs. 4127
F-test 9.232		Prob > F 0.000
Akaike crit. (AIC) 2432.67		Bayesian crit. (BIC) 12,560.27

*** p < 0.01, ** p < 0.05, * p < 0.1.

,

Table 9. Fixed Effects (GLS): Tobin’s Q.

Tobin’s Q	Coefficient	Std. Error	t-Value	p-Value
Constant	4.391	0.591	7.44	0.000 ***
ESG Score	0.021	0.003	6.16	0.000 ***
Log Total Asset	−0.345	0.059	−5.82	0.000 ***
Current Ratio	−0.026	0.026	−0.99	0.321
Financial Leverage	0.000	0.000	−0.01	0.994
Board Size	0.021	0.018	1.19	0.236
Board Independence	0.001	0.004	0.16	0.875
Board Diversity	0.018	0.003	6.10	0.000 ***
Mean dependent var 2.767		SD dependent var 2.110
R-squared 0.036		Number of Obs. 4127
F-test 19.555		Prob > F 0.000
Akaike crit. (AIC) 12,484.337		Bayesian crit. (BIC) 12,534.840

*** p < 0.01.

and

Table 10. Fixed Effects (GLS): Tobin’s Q by ESG Pillar.

Tobin’s Q	Coefficient	Std. Error	t-Value	p-Value
Constant	4.515	0.603	7.49	0.000 ***
Environmental (E)	0.010	0.004	2.52	0.012 **
Social (S)	0.013	0.004	3.14	0.002 ***
Governance (G)	0.029	0.005	5.67	0.000 ***
Log Total Asset	−0.352	0.060	−5.88	0.000 ***
Current Ratio	−0.028	0.027	−1.04	0.300
Financial Leverage	0.000	0.000	−0.03	0.977
Board Size	0.018	0.018	1.00	0.316
Board Independence	0.002	0.004	0.41	0.680
Board Diversity	0.016	0.003	5.37	0.000 ***
Mean dependent var 2.767		SD dependent var 2.110
R-squared 0.010		Number of Obs. 4127
F-test 18.555		Prob > F 0.000
Akaike crit. (AIC) 10,550.37		Bayesian crit. (BIC) 10,530.80

*** p < 0.01, ** p < 0.05.

. Table 7 and Table 9 present the fixed-effects GLS estimates using ROA and Tobin’s Q as dependent variables, respectively, with the composite ESG score as the key explanatory variable. To address reviewer concerns and provide further insight, Table 8 and Table 10 decompose the ESG score into its Environmental (E), Social (S), and Governance (G) pillars. This approach allows us to examine whether specific ESG dimensions drive the observed relationships. All regressions were estimated in Stata with firm fixed effects and robust standard errors, ensuring computational accuracy and consistency with panel data best practices. Model fit statistics (R-squared, F-test, AIC, and BIC) are reported at the bottom of each table.

4.4.1. Return on Assets (ROA)

Table 7 shows that ROA is positively associated with firm size (Log Total Assets), liquidity (Current Ratio), and board diversity, all statistically significant at the 5% or 1% levels. The composite ESG score has a positive but only marginally significant effect (p < 0.10), suggesting that sustainability initiatives have limited short-term impact on accounting profitability.

Table 8 extends this analysis by examining the E, S, and G components separately. The results indicate that the Social (S) pillar is marginally significant (p < 0.10) and Governance (G) shows a near-significant positive effect (p = 0.054), whereas Environmental (E) has no meaningful impact on ROA. These findings suggest that internal social policies and governance quality contribute more to operational profitability than environmental initiatives. The model R² improves slightly (1.9%), implying that pillar-level decomposition captures more of the variation in ROA than the aggregate ESG measure.

4.4.2. Tobin’s Q

The results in Table 9 confirm a strong positive association between the composite ESG score and Tobin’s Q (p < 0.01), indicating that capital markets reward firms with higher overall ESG performance. Board diversity also remains strongly significant, while firm size (Log Total Assets) is negatively related to Tobin’s Q, suggesting that smaller firms tend to enjoy higher relative valuations.

Table 10 reveals a more granular picture: all three ESG pillars are positively and significantly associated with Tobin’s Q, with Governance (G) exerting the largest effect (β = 0.029, p < 0.001). This implies that investors place particular value on strong governance systems, but also appreciate environmental and social initiatives as signals of long-term sustainability. The R² increases to 4.5%, further confirming that the pillar-level specification offers a better fit to the data.

Other variables, including the Current Ratio, Financial Leverage, Board Size, and Board Independence, do not exhibit statistically significant relationships with Tobin’s Q in this model. The R-squared value of 0.036 implies that the included variables explain 3.6% of the variation in Tobin’s Q, offering a modest improvement in explanatory power relative to the ROA model. The F-test indicates that the overall model is statistically significant, although the relatively low R-squared value suggests that further refinement may be necessary to better account for variation in firm valuation.

By contrast, the relatively weak correlation between ESG scores and ROA may lend support to Barnett’s [99] assertion that socially responsible investments can pose short-term costs that potentially reduce immediate profitability. Furthermore, economic volatility during the study period may have affected the observed relationships, adding a layer of complexity to the analysis. The potential for omitted variable bias should also be acknowledged, as unaccounted-for industry-specific characteristics could influence the link between ESG performance and financial outcomes [100]. Future research that incorporates industry-level controls or employs more granular datasets could offer a more comprehensive understanding of ESG’s financial implications in the U.S. context.

Collectively, the results in Table 7, Table 8, Table 9 and Table 10 provide robust evidence that ESG engagement is more strongly associated with market-based performance than with accounting profitability. The decomposition results highlight that the Governance pillar is the most powerful driver of Tobin’s Q, aligning with prior work suggesting that investors view governance quality as a signal of risk mitigation and long-term value creation (Eccles et al., 2014 [92]). The Social pillar contributes modestly to ROA, consistent with stakeholder theory predictions that employee and community relations can improve efficiency and morale, albeit with a delayed impact on financial results. The insignificant Environmental pillar effect on ROA supports Barnett’s [99] argument that environmental initiatives often entail upfront costs that reduce short-term profitability, though Table 10 indicates that markets still reward these efforts through higher valuations. These findings underline the importance of using both accounting- and market-based metrics to capture the full financial relevance of ESG practices and provide a richer theoretical basis for explaining heterogeneity in ESG–financial performance outcomes.

In conclusion, the hypothesis testing conducted via panel data regression, using two different financial performance metrics, reveals that ESG factors influence short-term and long-term outcomes differently. While the ESG score has a marginally positive relationship with ROA, its stronger association with Tobin’s Q suggests that capital markets view ESG engagement as a driver of long-term firm value. This divergence highlights the multifaceted impact of ESG practices and underscores the need to consider both accounting-based and market-based performance measures when evaluating the financial relevance of sustainability initiatives.

5. Conclusions

This study investigates the relationship between Environmental, Social, and Governance (ESG) factors and the financial performance of U.S. companies between 2013 and 2023. Drawing on panel data from 386 firms listed on the S&P 500, the analysis employs Return on Assets (ROA) and Tobin’s Q as measures of firm performance. The findings indicate a weak positive association between ESG scores and ROA, contrasted with a stronger positive relationship with Tobin’s Q. These results suggest that while ESG initiatives may not significantly enhance short-term profitability, they are positively perceived by the market, potentially contributing to long-term firm value.

The limited connection between ESG scores and ROA implies that the financial benefits of ESG practices may not be immediate. As ROA reflects short-term profitability, it may not fully capture the long-term value generated by ESG investments, which often entail substantial initial costs. This observation is consistent with the existing literature that highlights the financial burden firms may face when adopting ESG frameworks. In contrast, the more robust positive association with Tobin’s Q, a market-based performance measure, indicates that investors tend to view ESG-oriented firms more favourably, perceiving them as resilient and better positioned for sustained success.

These findings are in line with evidence from international markets. Velte [28] identified a similar positive link between ESG and firm performance in Germany, Yoon et al. [29] reported comparable outcomes for Korean firms, and Xie et al. [32] observed consistent global trends. Within the U.S. context, this study contributes additional insights into the differential effects of ESG factors on both short- and long-term financial performance.

From a theoretical perspective, the stronger association with Tobin’s Q supports stakeholder theory (Freeman, 2010 [56]), which posits that long-term success arises from addressing the interests of all stakeholders. ESG initiatives signal a firm’s strategic commitment to stakeholder engagement, potentially driving market value. Prior studies, including Barnett [98] and Crifo et al. [101], emphasise that the initial costs of implementing ESG strategies may suppress short-term profitability, particularly in capital-intensive sectors.

This study offers practical implications for both corporate managers and policy-makers. For business leaders, the findings suggest that, although ESG investments may not yield immediate financial returns, they play a critical role in driving long-term firm value, as reflected in market-based indicators like Tobin’s Q. For policy-makers, the positive market response to ESG performance underscores the importance of promoting sustainability and transparency, as such policies may contribute to long-term economic and corporate resilience.

However, several limitations must be acknowledged. A key constraint lies in the inconsistency of ESG reporting standards. Different ESG rating agencies adopt varied methodologies, making it difficult to uniformly assess corporate sustainability practices. This challenge has been noted in prior research by Chatterji et al. [17] and Amel-Zadeh and Serafeim [18], suggesting the need for future studies to examine the effects of differing ESG rating systems. Additionally, the analysis may be subject to omitted variable bias, as other unobserved factors, such as industry-specific characteristics or broader market dynamics, could influence the ESG–financial performance relationship. Future research that controls for these contextual factors would help provide a more comprehensive understanding of how ESG practices influence firm outcomes.

In conclusion, despite these limitations, this study contributes to the growing body of literature on ESG and firm performance by offering evidence from the U.S. market. In light of evolving regulatory developments, such as the SEC’s proposed climate-related disclosure rules [7], future research could explore how the standardisation of ESG reporting frameworks affects the relationship between ESG engagement and financial performance. Ultimately, the findings underscore the strategic value of ESG practices in enhancing long-term firm performance, providing meaningful insights for corporate governance and policy design.