ESG Performance and Firm Value: Evidence on Nonlinear Effects and Individual ESG Dimensions from European Union Listed Companies

Abstract

This study examines both the linear and nonlinear relationship between overall Environmental, Social, and Governance (ESG) performance and firm market value, while also comparing the effects of the Environmental, Social, and Governance dimensions in publicly listed companies from the European Union. The analysis is based on an unbalanced panel of 1706 non-financial listed firms covering the period 2011–2025. Firm value is primarily measured by Tobin’s Q, with the Price-to-Book ratio and Return on Assets (ROA) used for robustness analysis. The results indicate a significant U-shaped relationship between overall ESG performance and firm value, suggesting that the value-enhancing effects of ESG emerge only after firms achieve sufficiently high sustainability performance. In contrast, the individual Environmental, Social, and Governance dimensions in most cases do not exhibit significantly different effects on firm market value. Additional subsample analyses reveal that the nonlinear relationship is more pronounced among Western European firms and companies with lower greenhouse gas emissions intensity. The findings suggest that investors primarily evaluate firms based on their overall sustainability profile rather than individual ESG dimensions. The study contributes to the ESG literature by providing further evidence of the nonlinear nature of the ESG–firm value relationship and by comparing the explanatory power of aggregated and disaggregated ESG measures within the European Union’s harmonized sustainability reporting environment.

1. Introduction

Environmental, Social, and Governance (ESG) has emerged as a central framework for evaluating corporate sustainability and long-term business performance. ESG extends traditional financial assessment by incorporating three complementary dimensions: environmental performance, reflecting firms’ management of climate-related and resource challenges; social performance, encompassing relationships with employees, customers, suppliers, and communities; and governance performance, addressing corporate leadership, board effectiveness, shareholder rights, and ethical conduct (Shaikh, 2022; Drempetic et al., 2020). As sustainable investing has expanded globally, ESG information has become an increasingly important component of investment analysis, enabling investors to evaluate firms’ long-term growth potential, resilience, and risk exposure alongside conventional financial indicators (Amel-Zadeh & Serafeim, 2018; Postiglione et al., 2024). Nevertheless, ESG performance represents a multidimensional construct rather than a single homogeneous attribute. The Environmental, Social, and Governance dimensions capture distinct aspects of corporate sustainability and may therefore differ in their contribution to firm value. Consequently, the relationship between ESG performance and market valuation is increasingly recognized as heterogeneous, varying across individual ESG dimensions and institutional contexts and, in some cases, exhibiting nonlinear patterns (Ehlers et al., 2024). Since firm value reflects investors’ expectations regarding firms’ future growth opportunities, risk profile, and long-term competitive prospects, identifying which aspects of ESG are most value-relevant has become an important issue in the sustainability and corporate finance literature (Amel-Zadeh & Serafeim, 2018; Postiglione et al., 2024). In this context, the use of aggregated ESG scores may conceal substantial variation across the Environmental, Social, and Governance dimensions, motivating more disaggregated approaches to ESG assessment (Drempetic et al., 2020; Berg et al., 2022; Postiglione et al., 2024).

The existing literature on the relationship between Environmental, Social, and Governance (ESG) performance and firm value focuses on three major research issues. First, studies examine whether overall ESG performance enhances firm market value, producing mixed findings that range from significant positive effects to weak or insignificant relationships (Y. Li et al., 2018; Velte, 2017). Second, researchers investigate whether the Environmental, Social, and Governance dimensions contribute differently to firm value, suggesting that investors may assign greater importance to certain ESG pillars than others (Xie et al., 2019; Yu & Xiao, 2022; Baldissarro et al., 2026). Third, recent studies question the adequacy of aggregated ESG scores, arguing that they may conceal substantial variation across individual sustainability dimensions and advocating more disaggregated approaches to ESG assessment (Fatemi et al., 2018; Berg et al., 2022; Ehlers et al., 2024). In addition, recent evidence suggests that the relationship between ESG performance and firm value may be nonlinear, implying that the informational value of ESG performance and its effects on investor valuation may emerge only after firms achieve sufficiently high levels of ESG performance (Agarwala et al., 2024; Ikuta & Fujii, 2025). Accordingly, this study examines both the linear and nonlinear relationship between overall ESG performance and firm market value, while also comparing the effects of the Environmental, Social, and Governance dimensions in publicly listed companies from the European Union.

This study contributes to the existing ESG literature in several ways. First, it extends the literature on the ESG–firm value relationship by integrating both linear and nonlinear perspectives within a unified empirical framework, thereby providing additional evidence on whether the value relevance of ESG performance emerges only after firms achieve sufficiently high sustainability performance, complementing recent findings on nonlinear ESG effects (Agarwala et al., 2024; Siddiqui et al., 2024). Second, unlike many previous studies that primarily rely on aggregate ESG scores (Fatemi et al., 2018), this study directly compares the explanatory power of the overall ESG score with that of the individual Environmental, Social, and Governance dimensions in explaining firm market value, responding to recent calls for more disaggregated ESG assessment (Berg et al., 2022; Ehlers et al., 2024). Third, by focusing on publicly listed non-financial companies from the European Union over the period 2011–2025, this study provides additional evidence from a harmonized sustainability reporting environment and further explores whether the ESG–firm value relationship differs across regional and environmental contexts through analyses of Western and Eastern European firms and firms with different greenhouse gas emissions intensity, thereby extending prior evidence obtained in other institutional settings (Xie et al., 2019; Yu & Xiao, 2022; Baldissarro et al., 2026).

The remainder of the paper is organized as follows. The next section reviews the relevant theoretical and empirical literature and develops the research hypotheses. This is followed by the description of the data and methodology, the presentation and discussion of the empirical results, and, finally, the conclusions.

2. Literature Review

2.1. Theoretical Background

The relationship between ESG performance and firm value can be explained through several complementary theoretical perspectives. While some theories emphasize the role of ESG as a credible signal that influences investor perceptions and market valuation, others suggest that the Environmental, Social, and Governance dimensions represent distinct organizational capabilities and stakeholder relationships that may differ in their contribution to firm value. Collectively, these theoretical perspectives provide the foundation for examining both the overall (including potentially nonlinear) effect of ESG performance on firm value and the relative importance of the individual ESG dimensions.

Signaling Theory posits that firms communicate their underlying quality through credible signals that reduce information asymmetry between informed and uninformed market participants (Spence, 1978; Connelly et al., 2011). Consequently, superior ESG performance can strengthen investor confidence, reduce perceived uncertainty, and positively affect market valuation. Firms with strong sustainability practices exhibit better governance structures, more transparent non-financial reporting, and superior long-term stock market performance. Similarly, Y. Li et al. (2018) find that higher ESG disclosure enhances firm value by improving transparency and stakeholder trust, while Fatemi et al. (2018) demonstrate that ESG strengths increase firm value whereas ESG weaknesses reduce market valuation. Therefore, Signaling Theory suggests that ESG information constitutes a value-relevant signal that investors incorporate into firm valuation decisions.

Stakeholder Theory argues that firms create long-term value by effectively managing relationships with multiple stakeholder groups whose expectations are reflected differently across the Environmental, Social, and Governance dimensions (Donaldson & Preston, 1995; Freeman, 2010; Parmar et al., 2010). Consequently, investors may assign different value to individual ESG dimensions depending on the stakeholder interests and organizational capabilities they represent. Empirical evidence supports this perspective, showing that governance often exhibits the strongest association with firm performance and corporate efficiency (Velte, 2017; Xie et al., 2019), while other studies report heterogeneous effects across the Environmental, Social, and Governance dimensions (Yu & Xiao, 2022). Therefore, Stakeholder Theory provides the theoretical foundation for analysing the Environmental, Social, and Governance dimensions separately rather than relying solely on an aggregated ESG score.

Information Asymmetry Theory explains that corporate financial and non-financial disclosures reduce information disparities between managers and external investors, thereby improving investors’ ability to assess firm quality and reducing market uncertainty (Akerlof, 1978; Myers & Majluf, 1984; Healy & Palepu, 2001). Within the ESG context, sustainability reporting enhances transparency regarding environmental, social, and governance practices, supporting more informed investment decisions and increasing stakeholder trust (Y. Li et al., 2018; Amel-Zadeh & Serafeim, 2018). This perspective is particularly relevant in the European Union, where sustainability reporting has become increasingly institutionalized through regulations such as the NFRD, CSRD, and the EU Taxonomy, providing a relatively harmonized reporting environment for publicly listed companies (Candio, 2024; Drempetic et al., 2020).

2.2. Empirical Literature Review

The relationship between Environmental, Social, and Governance (ESG) performance and firm value has been extensively examined in the sustainability and corporate finance literature. Many empirical studies report a positive association between overall ESG performance and market valuation, suggesting that firms with stronger ESG practices benefit from greater investor confidence, enhanced transparency, improved risk management, and stronger long-term competitiveness (Y. Li et al., 2018; S. Wu et al., 2022; Z. Wu et al., 2024; Abdullah et al., 2026; Dkhili, 2024). Similar evidence is reported for European firms, where ESG practices are associated with stronger financial performance, particularly through environmental innovation, employment productivity, and diversity policies (De Lucia et al., 2020). Similar findings are reported by Yu and Xiao (2022), X. Li and You (2025), Zhao et al. (2025), Bai et al. (2025), He et al. (2025), and Tan et al. (2024), who document that higher ESG performance contributes positively to firm value across different institutional settings. However, the evidence is not entirely consistent. Velte (2017) finds no significant relationship between overall ESG performance and Tobin’s Q, while Candio (2024) reports mixed results depending on the financial performance measure employed. Likewise, Atan et al. (2018) find no significant relationship between either the combined ESG score or the individual ESG dimensions and Tobin’s Q for Malaysian listed firms. Likewise, Makridou et al. (2024) identify only limited evidence of a positive ESG–performance relationship in the European energy sector, and Hasan et al. (2022) report no significant association between environmental performance and market value in GCC banks. Thus, although the overall evidence is predominantly positive, the relationship between ESG performance and firm value remains inconclusive across countries, industries, and firm value measures. Similarly, Duque-Grisales and Aguilera-Caracuel (2021) document a negative relationship between ESG performance and firm financial performance.

Rather than treating ESG as a single construct, an increasing number of studies examine the Environmental, Social, and Governance dimensions separately, recognizing that each pillar may affect firm value differently. Empirical evidence suggests that governance frequently exhibits the strongest association with firm performance and market valuation (Velte, 2017; Xie et al., 2019; Tang et al., 2024), while other studies identify environmental performance as the primary driver of investor value, particularly in European financial firms (Baldissarro et al., 2026). In contrast, Yu and Xiao (2022) report significant positive effects for the environmental and social dimensions but find weaker evidence for governance, whereas Q. Li et al. (2026) document positive effects of environmental and social performance but a negative association between governance and firm value. Sectoral studies further indicate that the relative importance of individual ESG dimensions varies across industries. For example, environmental performance appears most influential in the energy sector (Makridou et al., 2024), while governance has the strongest effect in the airline industry (Abdi et al., 2022). Similarly, Broccardo et al. (2023) find that improvements in environmental and social sustainability are positively associated with firms’ economic performance, suggesting that not all ESG dimensions contribute equally to value creation. Moreover, recent evidence points to nonlinear and context-dependent relationships between individual ESG dimensions and firm value (Agarwala et al., 2024; Ikuta & Fujii, 2025; Rao et al., 2023). Overall, the existing literature indicates that the Environmental, Social, and Governance dimensions are not valued uniformly by capital markets, supporting the need to analyse their individual effects rather than relying solely on aggregated ESG scores.

Despite their widespread use, aggregated ESG scores have been increasingly criticized for their limited ability to capture firms’ sustainability performance accurately. One of the main concerns is the substantial divergence in ESG ratings across providers, which arises from differences in measurement methodologies, indicator selection, and weighting schemes, leading to inconsistent assessments of the same company (Berg et al., 2022; Chen & Wang, 2025). Moreover, Berg et al. (2021) demonstrate that historical ESG ratings may be revised over time, potentially affecting empirical findings and reducing the reproducibility of ESG-based research. Recent literature also argues that aggregate ESG scores combine fundamentally different sustainability attributes into a single indicator, making it difficult to identify which specific dimensions drive firm value or investment performance (Ehlers et al., 2024). Similarly, Postiglione et al. (2024) conclude that the lack of transparency and consistency in ESG rating methodologies remains one of the principal challenges in understanding the ESG–firm value relationship. Therefore, alternative representations of ESG performance that capture information beyond conventional aggregated scores deserve further investigation.

Although most studies report a positive association between ESG performance and firm value, recent evidence suggests that this relationship may not be strictly linear. Agarwala et al. (2024) document a U-shaped relationship between overall ESG disclosure and market performance, indicating that the value-enhancing effects of ESG become more pronounced only after firms reach a certain level of sustainability performance. Similarly, Ikuta and Fujii (2025) find that the relationship between ESG activities and financial performance varies across ESG dimensions and financial indicators, with nonlinear patterns observed in several cases. In addition, Candio (2024) reports that the ESG–financial performance relationship differs depending on the performance measure employed, further suggesting that the economic effects of ESG are context-dependent and may not follow a simple linear pattern.

Despite the rapid growth of ESG research, several important gaps remain in the existing literature. First, most empirical studies continue to rely on aggregated ESG scores when examining firm value, despite increasing recognition that investors use ESG information in diverse ways and often require more granular sustainability indicators for decision-making (Amel-Zadeh & Serafeim, 2018; Postiglione et al., 2024). Second, although a growing body of research investigates the Environmental, Social, and Governance dimensions separately, these studies primarily compare their relative effects rather than examining whether a firm’s weakest sustainability dimension provides incremental explanatory power beyond conventional ESG measures (Xie et al., 2019; Agarwala et al., 2024; Ikuta & Fujii, 2025). Third, recent evidence suggests that the value relevance of ESG varies considerably across institutional settings, industries, and performance measures, highlighting the need for additional evidence from the European regulatory environment (Gonçalves et al., 2026; Candio, 2024). Therefore, this study addresses these limitations by examining both the linear and nonlinear relationship between ESG performance and firm market value, while comparing the explanatory power of the overall ESG score and the individual Environmental, Social, and Governance dimensions in publicly listed European Union companies. Building on these theoretical perspectives and empirical findings, the following section develops the research hypotheses and outlines the empirical framework used to examine the linear and nonlinear effects of ESG performance, as well as the relative importance of the individual Environmental, Social, and Governance dimensions for firm market value.

3. Methodology

3.1. Conceptual Framework and Hypothesis Development

The conceptual framework integrates several theoretical perspectives to explain the relationship between ESG performance and firm market value. Signaling Theory provides the rationale for expecting ESG performance to influence market valuation, whereas Stakeholder Theory suggests that the Environmental, Social, and Governance dimensions may differ in their economic relevance because they represent distinct organizational capabilities and stakeholder interests. Information Asymmetry Theory further explains the informational role of ESG disclosure and justifies the focus on publicly listed companies operating within the European Union’s harmonized sustainability reporting environment. While these theoretical perspectives support the existence of an ESG–firm value relationship, recent empirical evidence suggests that this relationship may be nonlinear. Building on these theoretical foundations and recent empirical evidence, the study examines both the linear and nonlinear relationship between overall ESG performance and firm value, while comparing the effects of the individual Environmental, Social, and Governance dimensions, thereby extending the existing literature that has predominantly relied on aggregated ESG measures.

H1. 

ESG performance has a positive effect on the market value of publicly listed companies in the European Union, and this relationship is expected to be nonlinear.

The relationship between overall ESG performance and firm market value can be explained through several complementary theoretical perspectives. Signaling Theory suggests that strong ESG performance serves as a credible signal of corporate quality and long-term sustainability, thereby increasing investor confidence (Spence, 1978; Connelly et al., 2011). Similarly, Stakeholder Theory argues that firms creating value for a broad range of stakeholders are more likely to achieve superior long-term performance and market valuation (Freeman, 2010). Empirical evidence largely supports these theoretical arguments, with studies reporting a positive association between ESG performance and firm value measured by Tobin’s Q or other market-based indicators (Y. Li et al., 2018; Yu & Xiao, 2022; Alareeni & Hamdan, 2020). However, recent studies suggest that the benefits of ESG performance may not increase proportionally across all levels of ESG performance, indicating a potentially nonlinear relationship between ESG performance and firm value (Agarwala et al., 2024; Ikuta & Fujii, 2025). Therefore, this study examines both the linear and nonlinear effects of ESG performance on firm value.

H2. 

The Environmental, Social, and Governance dimensions differ in their effects on the market value of publicly listed companies in the European Union.

The Environmental, Social, and Governance dimensions may influence firm market value differently because each pillar reflects distinct organizational capabilities, stakeholder relationships, and sources of competitive advantage. Stakeholder Theory implies that each ESG dimension addresses the expectations of different stakeholder groups (Freeman, 2010). Consistent with these theoretical perspectives, empirical evidence indicates that the three ESG pillars do not have uniform effects on market valuation. For example, Velte (2017) and Xie et al. (2019) find that governance exhibits the strongest association with firm performance, whereas Yu and Xiao (2022) report significant differences in the effects of the Environmental, Social, and Governance dimensions on firm value. These findings suggest that analysing the ESG pillars separately may provide more informative insights than relying solely on an aggregated ESG score.

This study employs Tobin’s Q as the primary measure of firm value because it reflects investors’ expectations regarding a firm’s future growth opportunities and the market’s assessment of both financial and non-financial information, including ESG performance. Compared with accounting-based indicators such as ROA and ROE, Tobin’s Q is considered more appropriate for capturing the value relevance of sustainability initiatives, as these investments often generate long-term benefits that may not be immediately reflected in current accounting profitability (Velte, 2017; Alareeni & Hamdan, 2020). Accordingly, many recent ESG studies have adopted Tobin’s Q as their principal market-based measure of firm value when examining the capital market effects of corporate sustainability (Yu & Xiao, 2022; Baldissarro et al., 2026; Q. Li et al., 2026). Furthermore, Gerged et al. (2021) argue that market-based measures better capture investors’ valuation of sustainability-related disclosures than accounting-based indicators. For these reasons, Tobin’s Q is selected as the primary dependent variable in this study, while the Price-to-Book ratio is employed as a robustness measure to verify the consistency of the empirical findings.

The relationship between ESG performance and firm value may vary across firms with different institutional and environmental characteristics. Accordingly, this study complements the regional East–West European analysis with an alternative heterogeneity perspective based on firms’ greenhouse gas (GHG) emissions intensity. GHG emissions intensity not only reflects firms’ environmental impact but also captures differences in industry characteristics, as carbon-intensive sectors are generally subject to greater regulatory pressure, transition risks, and stakeholder scrutiny (Dkhili, 2024; Brighi et al., 2025). Consequently, investors may value improvements in ESG performance differently across firms with high and low emissions intensity. To explore this potential heterogeneity, the empirical analysis examines the ESG–firm value relationship separately for firms with high and low GHG emissions intensity, thereby providing an alternative perspective to the regional analysis and complementing recent evidence that the value relevance of ESG performance depends on firms’ environmental characteristics and sustainability context (Ehlers et al., 2024; Zhao et al., 2025).

3.2. Data and Variable Descriptions

This study employs an unbalanced panel dataset of publicly listed non-financial companies from European Union member states covering the period 2011–2025. Financial and market data, including Tobin’s Q, the Price-to-Book ratio, firm-specific control variables, and ESG scores, were obtained from Bloomberg Terminal. After excluding financial institutions and observations with missing financial or ESG information, the final sample comprised 1706 publicly listed non-financial companies included in the empirical analysis. The focus on European Union listed companies provides a consistent institutional setting, as sustainability reporting has become increasingly harmonized through regulatory initiatives such as the Non-Financial Reporting Directive (NFRD), the Corporate Sustainability Reporting Directive (CSRD), and the EU Taxonomy, thereby improving the comparability of ESG disclosures across firms. Annual GDP growth (GDP) and inflation (INF) were included as macroeconomic control variables. Both variables were obtained from the World Bank’s World Development Indicators database (World Bank, 2025a, 2025b).

Table 1. Variable description.

Variable/Name	Variable Description/Measurement	Source	Literature
Tobin’s Q (TobinQ)	Market value of equity plus total liabilities divided by total assets	Bloomberg Terminal	(Alareeni & Hamdan, 2020; Baldissarro et al., 2026)
Price-to-Book (PB)	Market capitalization divided by book value of equity	Bloomberg Terminal	(Yu & Xiao, 2022; Ikuta & Fujii, 2025)
Return on assets (ROA)	Net income divided by total assets	Bloomberg Terminal	(Shaikh, 2022; Candio, 2024)
ESG score (ESG)	Overall Environmental, Social and Governance score	Bloomberg ESG	(Agarwala et al., 2024; Ikuta & Fujii, 2025)
Environmental (ESGE)	Environmental pillar score	Bloomberg ESG	(Baldissarro et al., 2026)
Social (ESGS)	Social pillar score	Bloomberg ESG	(Atan et al., 2018)
Governance (ESGG)	Governance pillar score	Bloomberg ESG	(Velte, 2017; Xie et al., 2019)
Annual sales growth (Growth)	Annual sales growth, measured as the change in sales relative to the previous year	Bloomberg Terminal	(Gleißner et al., 2022)
Financial leverage (LEV)	Total assets divided by total equities	Bloomberg Terminal	(Baldissarro et al., 2026)
Current ratio (CR)	Current assets divided by current liabilities	Bloomberg Terminal	(Yurtoğlu & Gülşen, 2025)
Capital expenditure (CAPEX)	Capital expenditures scaled by total assets	Bloomberg Terminal	(Andrian, 2024)
Tangibility (TANG)	Tangible fixed assets divided by total assets	Bloomberg Terminal	(Bai et al., 2025)
Firm size (SIZE)	Natural logarithm of total assets in Euro	Bloomberg Terminal	(Q. Li et al., 2026)
GDP growth (GDP)	Annual GDP growth (%)	World Bank—World Development Indicators	(Siddiqui et al., 2024)
Inflation (INF)	Inflation, consumer prices (annual %)	World Bank—World Development Indicators	(Siddiqui et al., 2024)

presents the definitions, measurements, data sources, and supporting literature for all variables included in the empirical analysis.

The control variables were selected based on prior ESG and corporate finance literature to account for firm characteristics that may simultaneously influence ESG performance and firm value. Firm size is included because larger firms typically possess greater resources to invest in sustainability initiatives and are subject to higher public scrutiny. Leverage controls for differences in capital structure that may affect investors’ valuation. Firm growth is controlled for because growth opportunities influence market expectations and firm value. Annual GDP growth and inflation were included as macroeconomic control variables to account for changes in the broader economic environment that may influence firms’ performance, market valuation, and investment conditions. Observations with missing values for the dependent, independent, or control variables, including ESG scores and financial information required to construct the variables, were excluded from the analysis. Consequently, the final sample contains only observations with complete information.

To assess the robustness of the empirical findings, the baseline models estimated using Tobin’s Q were re-estimated employing the Price-to-Book ratio and Return on Assets (ROA) as alternative measures of firm performance. To examine the heterogeneity of the results, the baseline models were re-estimated for different subsamples. First, the sample was divided into Eastern and Western European Union countries, where the Eastern group comprises former socialist EU member states and the Western group includes the remaining EU countries. The Eastern European group comprised Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia, while the Western European group included the remaining EU member states. Second, firms were classified into high- and low-emission groups according to whether their greenhouse gas (GHG) emissions relative to sales were above or below the sample mean. Firms with GHG emissions intensity above the sample mean (0.187 tCO₂e/€1000 of sales) were classified as high-emission firms, whereas those with values equal to or below the sample mean were classified as low-emission firms. These additional analyses allow the relationship between ESG performance and firm value to be assessed across different institutional and environmental contexts. The GHG emissions subsample includes 1178 firms (4532 firm-year observations), compared with 1706 firms (7054 firm-year observations) in the baseline sample, because GHG emissions data were unavailable for all firms throughout the study period. Data preparation was performed using Microsoft Excel. The statistical and econometric analyses were conducted primarily using Gretl 2025b. StataNow 19.5 (BE-Basic Edition) was used to cross-check the estimation results and ensure their consistency.

3.3. Methods

The empirical analysis was conducted using a fixed-effects panel regression model to control for unobserved time-invariant firm-specific heterogeneity that may influence firm value. Firm-clustered robust standard errors were employed. The choice of the fixed-effects estimator was supported by the Hausman specification test (χ² = 45.72, p < 0.001), which indicated that the fixed-effects model was preferred over the random-effects model. Year fixed effects were also included to account for common macroeconomic shocks and time-specific influences.

Model 1 (H1). Linear and Nonlinear Effects of Overall ESG Performance on Firm Value:

$$Y_{it}= {\beta }_0 + {\beta }_1{ESG}_{it-1}+ {\beta }_2{ESG}_{it-1}^2+ X_{it-1}^{\prime }\gamma + {\mu }_i + {\epsilon }_{it}$$

(1)

Model 2 (H2). Effects of Individual Environmental, Social, and Governance Dimensions on Firm Value:

$$Y_{it}= {\beta }_0 + {\beta }_1{ESGE}_{it-1}+ {\beta }_2{ESGS}_{it-1}+ {\beta }_3{ESGG}_{it-1}+ X_{it-1}^{\prime }\gamma + {\mu }_i + {\epsilon }_{it}$$

(2)

where $Y_{it}$ denotes firm value measured by Tobin’s Q (and the Price-to-Book and ROA in the robustness analysis); $ES{G}_{it-1}$ represents the overall ESG score; $ESG{E}_{it-1}$, $ESG{S}_{it-1}$, and $ESG{G}_{it-1}$ denote the Environmental, Social, and Governance scores, respectively; $ES{G}_{it-1}^2$ is the squared ESG term used to examine the nonlinear relationship between ESG performance and firm value; $X_{it-1}^{\prime }$ is the vector of control variables; ${\mu }_i$ represents unobserved firm-specific fixed effects; ${\lambda }_t$ denotes year fixed effects; and ${\epsilon }_{it}$ is the idiosyncratic error term.

To mitigate potential endogeneity arising from unobserved firm-specific heterogeneity, the regression models employ firm fixed effects. This approach controls for time-invariant firm characteristics that may simultaneously influence ESG performance and firm value, thereby reducing omitted variable bias. Year fixed effects are also included to control for common macroeconomic and regulatory shocks affecting all firms over time. Although these procedures help mitigate endogeneity concerns, they cannot completely eliminate the possibility of reverse causality or other sources of endogeneity.

To strengthen the robustness of the empirical estimates, robust standard errors were employed to account for potential heteroskedasticity, serial correlation, and cross-sectional dependence in the panel data. In addition, multicollinearity was assessed prior to model estimation and was not found to affect the validity of the regression results.

4. Results

Descriptive statistics for the study variables are reported in Appendix A (

Table A1. Descriptive statistics for Western and Eastern European Union countries.

Variable	Mean	Median	Std. Dev.	Mean	Median	Std. Dev.	Min	Max
	Western EU			Eastern EU			All Panels
TobinQ	1.60	1.23	1.04	1.33	1.06	0.94	0.49	5.83
PB	2.58	1.67	2.60	1.97	1.15	2.39	0.22	11.80
ROA	3.80	3.86	6.64	5.06	4.58	7.83	−18.80	24.00
ESG	3.32	3.28	1.47	2.18	1.92	1.30	0.23	8.56
ESGE	2.92	2.81	2.20	1.72	1.25	1.80	0.00	10.00
ESGS	2.69	2.24	1.94	1.96	1.50	1.76	0.00	10.00
ESGG	5.31	5.48	1.45	3.56	3.40	1.27	0.70	8.85
Growth	0.08	0.06	0.21	0.12	0.09	0.25	−0.52	0.73
LEV	3.19	2.55	1.98	2.58	2.06	1.76	1.21	10.40
CR	1.68	1.37	1.11	1.89	1.44	1.38	0.32	6.67
CAPEX	0.04	0.03	0.03	0.05	0.04	0.04	0.00	0.16
TANG	0.79	0.85	0.20	0.91	0.98	0.15	0.25	1.00
Size	21.50	21.60	2.04	19.60	19.30	1.73	16.90	25.80
X1	1.74	1.65	2.91	2.99	3.03	2.45	−8.20	9.48
X4	3.43	2.42	2.65	8.16	9.12	5.05	−1.74	15.20

Notes: Source: authors’ calculations.

and

Table A2. Descriptive statistics for firms with low and high greenhouse gas (GHG) emissions intensity.

Variable	Mean	Median	Std. Dev.	Mean	Median	Std. Dev.	Min	Max
	Low-GHG Panel			High-GHG Panel			All Panels
TobinQ	1.67	1.27	1.09	1.22	1.11	0.54	0.49	5.83
PB	2.76	1.81	2.67	1.73	1.33	1.54	0.22	11.80
ROA	4.45	4.13	6.05	4.19	4.06	5.72	−18.80	24.00
ESG	3.65	3.60	1.28	3.89	3.83	1.25	0.23	8.56
ESGE	3.47	3.35	2.06	3.37	3.35	1.75	0.00	10.00
ESGS	2.96	2.56	1.87	3.41	3.20	1.84	0.00	10.00
ESGG	5.52	5.62	1.34	5.50	5.62	1.24	0.70	8.85
Growth	0.08	0.06	0.18	0.08	0.04	0.24	−0.52	0.73
LEV	3.24	2.61	1.99	3.12	2.46	1.94	1.21	10.40
CR	1.54	1.30	0.93	1.56	1.34	0.87	0.32	6.67
CAPEX	0.04	0.03	0.03	0.06	0.05	0.03	0.00	0.16
TANG	0.77	0.82	0.19	0.89	0.95	0.14	0.25	1.00
Size	21.70	21.80	1.88	22.60	22.70	1.65	16.90	25.80
GDP	1.83	1.71	3.01	1.85	1.96	3.32	−8.20	9.48
INF	3.82	2.77	3.26	3.14	2.00	3.10	−1.74	15.20

Notes: Source: authors’ calculations.

). Table A1 presents the descriptive statistics for the Western and Eastern European Union subsamples, while Table A2 reports the corresponding statistics for firms classified according to greenhouse gas (GHG) emissions intensity. The pairwise correlation matrix is presented in

Table A3. Correlation matrix.

Variable	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
TobinQ (1)	1
PB (2)	0.834	1
ROA (3)	0.392	0.279	1
ESG (4)	0.013	0.039	0.048	1
ESGE (5)	0.02	0.051	0.069	0.816	1
ESGS (6)	−0.071	−0.048	0.02	0.767	0.418	1
ESGG (7)	0.101	0.098	0.002	0.673	0.431	0.369	1
Growth (8)	0.143	0.132	0.204	−0.061	−0.046	−0.035	−0.069	1
LEV (9)	−0.122	0.161	−0.226	0.102	0.108	0.056	0.068	0.012	1
CR (10)	0.113	−0.016	0.148	−0.186	−0.183	−0.114	−0.147	−0.004	−0.358	1
CAPEX (11)	0.019	−0.002	0.118	0.023	0.019	0.003	−0.045	0.02	−0.084	−0.077	1
TANG (12)	−0.186	−0.184	0.025	−0.103	−0.154	0.067	−0.228	0.003	0.011	0.207	0.24	1
Size (13)	0.014	0.056	0.064	0.588	0.467	0.432	0.5	−0.03	0.203	−0.253	0.024	−0.163	1
GDP (14)	−0.007	−0.019	0.104	−0.08	−0.06	−0.061	−0.077	0.268	−0.005	−0.005	0.022	0.029	−0.063	1
INF (15)	−0.109	−0.12	0.012	−0.233	−0.165	−0.159	−0.28	0.231	−0.048	0.069	0.042	0.163	−0.357	0.215	1

Notes: Source: authors’ calculations.

. The correlation coefficients are generally low to moderate, with the highest correlations observed among the ESG-related variables, reflecting their conceptual similarity.

Several diagnostic tests were performed to assess the suitability of the econometric specifications. The Breusch–Pagan Lagrange multiplier test rejected the null hypothesis of no firm-specific effects for the Price-to-Book, ROA, and Tobin’s Q models (all p < 0.001), indicating that pooled OLS was inappropriate. The Hausman test also rejected the null hypothesis that the random-effects estimator was consistent in all three models: Price-to-Book χ²(20) = 260.799, ROA χ²(20) = 64.354, and Tobin’s Q χ²(20) = 172.036, with all p < 0.001. These results support the use of firm fixed-effects models. Multicollinearity was assessed using variance inflation factors, with the maximum VIF equal to 4.006, indicating that serious multicollinearity was not a concern. Finally, the Wooldridge test rejected the null hypothesis of no first-order serial correlation in all three models (all p < 0.001). Accordingly, firm-level clustered standard errors were used to account for within-firm serial correlation and potential heteroskedasticity.

Table 2. Nonlinear effects of ESG performance on firm value: Evidence from Western and Eastern European Union countries.

Variable	TobinQ		PB		ROA
	Western EU	Eastern EU	Western EU	Eastern EU	Western EU	Eastern EU
ESG	−0.164 *** (0.048)	−0.136 (0.086)	−0.202 (0.126)	−0.10912	0.151 (0.356)	−2.464 ** (1.124)
ESG2	0.019 *** (0.006)	0.009 (0.013)	0.028 * (0.016)	0.049 (0.036)	−0.001 (0.042)	0.388 ** (0.179)
Growth	0.342 *** (0.048)	0.198 *** (0.068)	0.586 *** (0.120)	0.361 ** (0.157)	5.847 *** (0.538)	7.250 *** (1.213)
LEV	−0.00023	−0.052 (0.040)	0.298 *** (0.041)	0.519 *** (0.144)	−0.605 *** (0.121)	−1.151 *** (0.416)
CR	−0.033 (0.027)	0.001 (0.044)	−0.132 ** (0.053)	−0.097 (0.100)	0.961 *** (0.204)	2.459 *** (0.590)
CAPEX	2.132 *** (0.441)	0.595 (0.494)	3.304 *** (1.073)	2.737 * (1.426)	24.456 *** (5.064)	19.594 * (11.482)
TANG	1.034 *** (0.309)	1.063 (0.738)	1.873 *** (0.587)	1.614 (1.811)	7.358 *** (2.662)	22.255 ** (8.979)
Size	−0.430 *** (0.060)	−0.143 (0.145)	−1.559 *** (0.150)	−0.948 ** (0.392)	−0.057 (0.789)	5.690 *** (1.788)
GDP	0.001 (0.004)	0.005 (0.014)	0.002 (0.012)	0.021 (0.046)	−0.051 (0.046)	0.194 (0.222)
INF	−0.008 (0.005)	0.009 (0.010)	0.023 (0.015)	0.000 (0.040)	−0.048 (0.067)	−0.190 (0.199)
Constant	10.290 *** (1.279)	3.561 (3.213)	33.959 *** (3.210)	18.576 ** (8.812)	−2.495 (18.197)	−127.466 *** (36.381)
Observations	6045	1009	6045	1009	6045	1009
Number of firms	1385	321	1385	321	1385	321
Within R2	0.179	0.112	0.207	0.183	0.131	0.237
LSDV R2	0.893	0.916	0.867	0.910	0.701	0.724

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses. Source: Authors’ calculations.

presents the results for the Eastern and Western European Union subsamples. The findings indicate that the nonlinear relationship between ESG performance and firm value is evident among firms from Western European countries. For these firms, the ESG coefficient is negative and the squared ESG term is positive and statistically significant when firm value is measured by Tobin’s Q, indicating a U-shaped relationship. A similar pattern is observed for the Price-to-Book ratio, although the nonlinear effect is weaker. In contrast, no significant nonlinear relationship is found for firms from Eastern European countries when market-based measures are used. When ROA is employed as the dependent variable, significant coefficients are observed only for the Eastern European subsample, suggesting that ESG performance may have a different association with accounting-based performance than with market valuation. To verify the estimated nonlinear relationship, the turning point was calculated as $TP=-{\beta }_1/\left(2{\beta }_2\right)$ and compared with the observed range of ESG values. The estimated turning points are 4.32 for Tobin’s Q (Western EU) and 3.18 for ROA (Eastern EU). Both values lie within the observed ESG range (0.23–8.56), supporting the existence of a U-shaped relationship in these models. Overall, the results suggest that the ESG–firm value relationship differs across European regions, with stronger evidence of nonlinear market valuation effects in Western European firms.

Table 3. Nonlinear effects of ESG performance on firm value by greenhouse gas (GHG) emissions intensity.

Variable	TobinQ		PB		ROA
	Low GHG	High GHG	Low GHG	High GHG	Low GHG	High GHG
ESG	−0.162 ** (0.065)	−0.086 (0.079)	−0.094 (0.189)	0.042 (0.221)	0.149 (0.389)	−0.616 (1.170)
ESG2	0.018 ** (0.008)	0.004 (0.009)	0.016 (0.024)	−0.012 (0.026)	−0.022 (0.047)	0.115 (0.130)
Growth	0.384 *** (0.058)	0.069 * (0.041)	0.722 *** (0.158)	0.280 ** (0.142)	6.218 *** (0.659)	6.784 *** (1.123)
LEV	−0.043 *** (0.015)	0.031 ** (0.013)	0.331 *** (0.063)	0.387 *** (0.053)	−0.702 *** (0.137)	−0.631 ** (0.317)
CR	−0.053 (0.064)	−0.013 (0.024)	−0.131 (0.123)	−0.103 (0.094)	0.609 ** (0.283)	1.433 *** (0.502)
CAPEX	3.802 *** (0.685)	1.533 *** (0.560)	7.118 *** (1.733)	3.961 *** (1.245)	13.210 * (6.775)	32.472 *** (10.517)
TANG	1.164 *** (0.405)	0.885 ** (0.386)	1.596 * (0.893)	1.072 (1.002)	6.841 ** (2.923)	7.462 (5.966)
Size	−0.358 *** (0.072)	−0.326 (0.209)	−1.336 *** (0.208)	−1.573 *** (0.333)	0.370 (0.751)	−1.057 (1.288)
GDP	−0.000 (0.006)	0.003 (0.006)	−0.001 (0.016)	0.006 (0.016)	−0.00353	−0.065 (0.093)
INF	−0.021 *** (0.005)	0.006 (0.006)	−0.018 (0.021)	0.038 * (0.019)	−0.034 (0.065)	−0.250 (0.160)
Constant	8.952 *** (1.568)	7.914 * (4.586)	29.567 *** (4.604)	35.165 *** (7.526)	−8.859 (17.240)	20.155 (29.601)
Observations	3656	876	3656	876	3656	876
Number of firms	975	203	975	203	975	203
Within R2	0.163	0.185	0.175	0.418	0.139	0.225
LSDV R2	0.906	0.865	0.868	0.876	0.754	0.560

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses. Source: Authors’ calculations.

reports the results for firms classified according to their greenhouse gas (GHG) emissions relative to sales. The nonlinear relationship between ESG performance and firm value is observed only among firms with lower GHG emissions, where the ESG coefficient is negative and the squared ESG term is positive and statistically significant when Tobin’s Q is used as the dependent variable, indicating a U-shaped relationship. No comparable nonlinear effect is found for firms with higher GHG emissions or when the Price-to-Book ratio and ROA are employed. The estimated turning point for the Tobin’s Q model in the low-GHG-emissions-intensity subsample is 4.50. As this value lies within the observed ESG range (0.23–8.56), the estimated nonlinear relationship can be interpreted as U-shaped. Overall, the findings suggest that the relationship between ESG performance and firm value is more pronounced among firms with lower carbon intensity, following a U-shaped pattern.

Table 4. Effects of individual ESG dimensions on firm value: Western and Eastern European Union.

Variable	TobinQ		PB		ROA
	Western EU	Eastern EU	Western EU	Eastern EU	Western EU	Eastern EU
ESGE	−0.012 (0.009)	−0.013 (0.019)	−0.005 (0.027)	−0.00603	0.178 ** (0.086)	0.070 (0.239)
ESGS	−0.000 (0.010)	−0.036 ** (0.015)	0.013 (0.027)	−0.021 (0.037)	−0.046 (0.087)	−0.211 (0.243)
ESGG	−0.023 (0.018)	−0.017 (0.042)	−0.024 (0.043)	0.011 (0.108)	0.044 (0.143)	−0.153 (0.427)
Growth	0.346 *** (0.048)	0.203 *** (0.069)	0.592 *** (0.121)	0.372 ** (0.164)	5.868 *** (0.538)	7.333 *** (1.221)
LEV	−0.022 ** (0.011)	−0.056 (0.041)	0.297 *** (0.040)	0.518 *** (0.145)	−0.598 *** (0.121)	−1.172 *** (0.410)
CR	−0.033 (0.028)	−0.002 (0.044)	−0.133 ** (0.053)	−0.097 (0.101)	0.966 *** (0.203)	2.410 *** (0.591)
CAPEX	2.153 *** (0.439)	0.613 (0.506)	3.338 *** (1.067)	2.671 * (1.451)	24.318 *** (5.050)	18.976 (11.612)
TANG	1.025 *** (0.311)	1.019 (0.726)	1.866 *** (0.592)	1.363 (1.785)	7.360 *** (2.650)	21.356 ** (9.063)
Size	−0.435 *** (0.058)	−0.167 (0.150)	−1.567 *** (0.150)	−1.009 ** (0.411)	−0.068 (0.780)	5.161 *** (1.770)
GDP	0.001 (0.004)	0.006 (0.013)	0.001 (0.012)	0.019 (0.046)	−0.052 (0.046)	0.236 (0.227)
INF	−0.007 (0.005)	0.009 (0.010)	0.024 (0.016)	0.004 (0.040)	−0.045 (0.067)	−0.175 (0.203)
Constant	10.273 *** (1.237)	3.978 (3.305)	33.935 *** (3.212)	19.517 ** (9.099)	−2.469 (18.117)	−118.366 *** (36.133)
Observations	6045	1009	6045	1009	6045	1009
Number of firms	1385	321	1385	321	1385	321
Within R2	0.174	0.107	0.206	0.181	0.132	0.231
LSDV R2	0.893	0.915	0.866	0.910	0.701	0.722

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses. Source: Authors’ calculations.

presents the results for the individual ESG dimensions across the Western and Eastern European Union subsamples. Overall, the findings provide limited evidence that the Environmental, Social, and Governance dimensions differ in their effects on firm value. None of the individual ESG dimensions is significantly associated with the market-based measures of firm value (Tobin’s Q and Price-to-Book) in Western European firms. In the Eastern European subsample, only the Social dimension exhibits a significant negative association with Tobin’s Q, while the Environmental dimension is positively related to ROA in Western European firms. Overall, the results suggest that individual ESG dimensions have limited and inconsistent explanatory power across regional subsamples, providing little support for systematic differences in their effects on firm value.

Table 5. Effects of individual ESG dimensions on firm value by greenhouse gas (GHG) emissions intensity.

Variable	TobinQ		PB		ROA
	Low GHG	High GHG	Low GHG	High GHG	Low GHG	High GHG
ESGE	0.004 (0.012)	−0.025 (0.016)	0.025 (0.035)	0.017 (0.037)	0.197 ** (0.094)	0.398 (0.352)
ESGS	−0.010 (0.013)	−0.012 (0.011)	0.019 (0.038)	−0.052 (0.033)	−0.01226	−0.085 (0.238)
ESGG	−0.024 (0.024)	−0.028 (0.019)	−0.036 (0.055)	−0.042 (0.056)	−0.092 (0.167)	−0.184 (0.434)
Growth	0.386 *** (0.059)	0.072 * (0.041)	0.723 *** (0.159)	0.279 ** (0.140)	6.195 *** (0.654)	6.914 *** (1.107)
LEV	−0.043 *** (0.015)	0.029 ** (0.012)	0.331 *** (0.062)	0.385 *** (0.055)	−0.688 *** (0.135)	−0.645 ** (0.309)
CR	−0.055 (0.065)	−0.013 (0.024)	−0.135 (0.124)	−0.105 (0.094)	0.602 ** (0.282)	1.479 *** (0.510)
CAPEX	3.801 *** (0.680)	1.508 *** (0.572)	7.078 *** (1.725)	3.797 *** (1.262)	12.934 * (6.822)	31.111 *** (10.262)
TANG	1.152 *** (0.407)	0.916 ** (0.384)	1.599 * (0.894)	1.019 (1.023)	6.911 ** (2.897)	8.081 (6.013)
Size	−0.365 *** (0.070)	−0.326 (0.203)	−1.337 *** (0.207)	−1.551 *** (0.338)	0.374 (0.744)	−1.143 (1.281)
GDP	−0.000 (0.006)	0.003 (0.005)	−0.002 (0.016)	0.008 (0.016)	−0.00357	−0.075 (0.091)
INF	−0.019 *** (0.005)	0.006 (0.006)	−0.016 (0.021)	0.038 ** (0.019)	−0.033 (0.064)	−0.241 (0.158)
Constant	8.934 *** (1.527)	7.924 * (4.522)	29.538 *** (4.567)	35.036 *** (7.662)	−8.621 (17.171)	21.170 (29.706)
Observations	3656	876	3656	876	3656	876
Number of firms	975	203	975	203	975	203
Within R2	0.159	0.187	0.175	0.42	0.142	0.226
LSDV R2	0.906	0.865	0.868	0.877	0.755	0.561

Note: ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Robust standard errors are reported in parentheses. Source: Authors’ calculations.

reports the results for firms grouped according to greenhouse gas (GHG) emissions relative to sales. Overall, the individual Environmental, Social, and Governance dimensions do not exhibit statistically significant associations with the market-based measures of firm value in either subsample. The only significant ESG-related effect is a positive relationship between the Environmental dimension and ROA among firms with lower GHG emissions. These findings indicate that the explanatory power of the individual ESG dimensions remains limited across firms with different levels of carbon intensity, providing little evidence that their effects on firm value vary systematically between the two groups.

Overall, the empirical results indicate that the relationship between ESG performance and firm value is more complex than suggested by conventional linear models. While the findings support a nonlinear association between overall ESG performance and market valuation, they provide only limited evidence that the individual Environmental, Social, and Governance dimensions differ in their effects on firm value. Furthermore, the subsample analyses reveal that these relationships vary across institutional and environmental contexts, particularly between Western and Eastern European countries and between firms with different levels of greenhouse gas emissions. These findings are discussed in relation to the existing literature in the following section.

5. Discussion

The findings partially support the first hypothesis by indicating that the relationship between ESG performance and firm market value is nonlinear rather than purely linear. While most previous studies report a positive association between ESG performance and firm value, the present findings suggest that the economic benefits of ESG may emerge only beyond certain levels of sustainability performance. This result is consistent with Agarwala et al. (2024), who document a U-shaped relationship between ESG disclosure and market performance, and with Ikuta and Fujii (2025), who report nonlinear relationships between ESG activities and financial performance across different ESG dimensions. Furthermore, the observed differences between Tobin’s Q and the robustness measures are consistent with Candio (2024), who demonstrates that the ESG–financial performance relationship depends on the performance indicator employed. Collectively, these findings suggest that linear specifications may underestimate the complexity of the ESG–firm value relationship.

Contrary to the second hypothesis, the individual Environmental, Social, and Governance dimensions do not exhibit statistically significant differences in explaining firm market value. This finding contrasts with Yu and Xiao (2022), who report heterogeneous effects across ESG pillars, and with Baldissarro et al. (2026), who identify environmental performance as the primary driver of firm value in European financial institutions. Similarly, Velte (2017) and Xie et al. (2019) find governance to be the most value-relevant ESG dimension. However, the present results are broadly consistent with Atan et al. (2018), who report no significant relationship between either the aggregate ESG score or the individual ESG dimensions and Tobin’s Q. One possible explanation is that investors in the European Union increasingly evaluate firms based on their overall sustainability profile rather than emphasizing individual ESG dimensions, particularly within a relatively harmonized sustainability reporting environment. Another possible explanation is that the relationship between the individual Environmental, Social, and Governance dimensions and firm value may also be nonlinear, implying that linear model specifications may fail to capture their value relevance.

From a theoretical perspective, the findings suggest that ESG performance creates value primarily through its role as a credible market signal rather than through the isolated effects of individual sustainability dimensions. Consistent with Signaling Theory and Information Asymmetry Theory, firms with relatively low levels of ESG performance may initially incur the costs of sustainability investments without generating sufficiently credible signals to influence investor perceptions, resulting in limited or even negative valuation effects. As ESG performance improves and reaches a sufficiently high level, however, sustainability disclosures become more credible and informative, reducing information asymmetry and strengthening investor confidence, thereby enhancing firm value. At the same time, the absence of significant differences across the Environmental, Social, and Governance dimensions may indicate that investors evaluate ESG performance as an integrated strategic capability, consistent with the Stakeholder Theory, rather than assigning substantially different value to individual sustainability pillars.

This study has several limitations that should be acknowledged. First, the analysis focuses exclusively on publicly listed companies in the European Union, which may limit the generalizability of the findings to non-listed firms and other institutional settings. Second, ESG performance is measured using Bloomberg ESG scores, which, despite their widespread use, may be influenced by provider-specific methodologies and rating approaches. Consequently, the reported relationships may partly reflect the characteristics of a particular ESG rating methodology rather than universally applicable measures of corporate sustainability. A further limitation of this study is that it focuses on estimating the relationship between ESG performance and firm value rather than identifying the specific mechanisms through which ESG creates firm value. As a result, the empirical analysis does not distinguish whether the observed effects are driven primarily by improvements in corporate reputation, lower financing costs, enhanced innovation capacity, better risk management, or other value-creating channels. Moreover, although several robustness checks were performed, the observational nature of the data does not allow definitive causal conclusions regarding the ESG–firm value relationship. Future research could extend the present study by employing alternative econometric approaches, such as instrumental variable methods or two-stage least squares (2SLS) estimation, to further address potential endogeneity between ESG performance and firm value. As longer time series of ESG data become available, future studies could also employ dynamic panel data models to better account for persistence and endogeneity, while providing a more comprehensive assessment of nonlinear and potentially asymmetric relationships between ESG performance and firm value. Future studies could also investigate the mechanisms linking ESG performance to firm value by explicitly examining mediating factors such as corporate reputation, financing costs, innovation, and risk management. Additional evidence could further be obtained by investigating industry-specific subsamples, firm size categories, and potential nonlinear relationships between the individual Environmental, Social, and Governance dimensions and firm value.

6. Conclusions

This study examined the relationship between ESG performance and firm market value using a sample of publicly listed companies from the European Union. Specifically, it investigated whether overall ESG performance influences market valuation, including the possibility of a nonlinear relationship, and whether the Environmental, Social, and Governance dimensions differ in their effects on firm value.

The empirical findings provide several important conclusions. First, the results indicate that overall ESG performance is associated with firm market value through a significant nonlinear relationship, suggesting that the value relevance of ESG cannot be fully captured using conventional linear models. Second, the analysis does not provide evidence that the Environmental, Social, and Governance dimensions exert significantly different effects on market valuation. Although previous studies have frequently reported heterogeneous effects across individual ESG pillars, the present findings suggest that, within the European Union context, investors primarily respond to firms’ overall sustainability performance rather than to individual ESG dimensions. The robustness analysis using the Price-to-Book ratio supports the main conclusions, reinforcing the reliability of the empirical results.

The study contributes to the ESG literature in several ways. First, it provides additional evidence that the relationship between ESG performance and firm value may be nonlinear, extending the predominantly linear perspective adopted in previous research. Second, it offers a direct comparison between the explanatory power of the overall ESG score and the individual Environmental, Social, and Governance dimensions, demonstrating that aggregated ESG performance remains more informative for explaining market valuation within the examined sample. Third, by focusing on publicly listed companies operating within the European Union, the study provides evidence from a relatively harmonized sustainability reporting environment shaped by recent regulatory developments.

The findings also have practical implications for corporate managers, investors, and policymakers. For managers, the results suggest that developing a consistent overall ESG profile may be more important for market valuation than emphasizing individual sustainability dimensions in isolation. For investors, the findings highlight the importance of considering potential nonlinearities when incorporating ESG information into investment decisions. For policymakers, the results provide further support for the continuing development of harmonized ESG reporting standards that improve the transparency and comparability of sustainability information.

Several limitations should be acknowledged. The analysis is limited to publicly listed companies from European Union member states and therefore may not be directly generalizable to other institutional settings. In addition, the study relies on ESG ratings as proxies for corporate sustainability performance, despite ongoing concerns regarding differences in ESG rating methodologies across providers. Future research could examine nonlinear ESG–firm value relationships in other geographical regions, compare alternative ESG rating providers, and investigate whether nonlinear effects vary across industries or firm life-cycle stages.