Exploring the Impact of Sustainability, Board Characteristics, and Firm-Specifics on Firm Value: A Comparative Study of the United Kingdom and Turkey

Abstract

The study aims to investigate the effect of several sustainability indicators on firms’ value. Panel data of 1914 observations from the UK and Turkey from 2016 to 2021 with a fixed effect model are used to estimate the results. The findings reveal that ESG indicators associate significantly with firms’ value. However, ESG indicators exhibit a stronger significant association with Tobin’s Q than stock prices and market-to-book value. This indicates that sustainability indicators are linked to the firm’s overall market value and the long-term run market valuation rather than just the stock market value. The results also reveal that while board independence, board expertise, and diversity exhibit a significant and positive association with firms’ value, board size negatively affects firms’ value. The current study provides unique contributions and comprehensive evidence based on different institutional and country sustainability enforcement statuses. It offers empirical implications for regulatory authorities and other developing countries to provide a comprehensive ESG reporting framework.

1. Introduction

In recent decades, ESG disclosures have become more common among publicly traded corporates as they strive to incorporate stakeholders, respond to investor pressure, gain credibility, and respond to industry-wide crises and competitive moves [1]. Yu et al. [2] indicate that according to the “Governance and Accountability Institute” (2017), 82 percent of “S&P 500” corporates reported on sustainability in 2017, compared to only 53 percent in 2012. Thus, firms’ value (FV) could be influenced by several factors, including continuous ESG metric monitoring, forming new sustainability initiatives, and the annual disclosure of sustainability reports [3]. By 2030, all companies are expected to disclose information regarding their environmental and social impact in accordance with the “United Nations Sustainable Stock Exchange Initiative” (Sustainable Stock Exchanges, 2015). Eccles and Youmans [4] state that investors go beyond financial information to require non-financial information, including ESG issues. Thus, to reveal this material and complex non-financial information, the board of directors have to develop integrated reports and select the targeted financial information users to create value over the short-, medium-, and long-term value.

In Turkey, social and environmental reporting is entirely voluntary, and no government regulation governs non-financial reporting. In 2014, Borsa Istanbul (BIST) initiated BIST Sustainability Index. The index comprises publicly traded companies with strong corporate sustainability performance. The BIST Index allows institutional investors to demonstrate their commitment to companies that effectively manage “environmental, social, and governance” (ESG) challenges. The index aims to raise awareness, knowledge, and implementation of ESG in Turkey [5]. In the case of the UK, the UK government made significant efforts toward ESG reporting, requiring firms to comply with some regulations in this regard. According to “The Companies Act 2006/414c”, companies must include information on their carbon emissions in their strategy report rather than their business review. These guidelines apply to large and medium-sized publicly traded firms and small and medium-sized private-sector businesses. The Act underlined that failing to report on environmental, social, and employee issues in the strategy report may subject the corporation to a penalty. However, the Act and its amendments do not provide specific guidance on CSR reporting indicators that firms should comply [6]. Albitar et al. [7] indicate that UK firms have a strong incentive to voluntarily incorporate ESG information into corporate reports to meet stakeholders’ needs.

Several pieces of research have been conducted to explore several sustainability and environmental issues [8,9,10,11,12]. However, these studies did not assess the relationship between ESG performance and firms’ value. On the other hand, some studies exist that assess the association between ESG performance and firms’ value. However, the results of these studies are ambiguous, due in part to measurement concerns or data limits and in part to model misspecification [13]. Regarding the measurement concerns, FV has been measured using several measures. The majority of studies utilized Tobin’s Q [2,3,13,14,15,16,17,18,19,20,21,22]. Some other studies [23,24,25] used stock prices, and Abdi et al. [18,22] and Al-Shaer [26] used market-to-book value as proxies for firms’ value. Despite the different views and plenty of studies on the relationship between ESG disclosures and firms’ value, the results are either inconclusive or mixed [19]. Accordingly, the present study goes beyond prior studies to comprehensively investigate the impact of ESG and sustainability performance on firms’ value the impact of ESG and sustainability performance on firms’ value. This study is motivated by many factors, which contribute to the literature in many ways. First, we provide a comprehensive investigation of the influence of ESG on FV based on a comparative analysis of two countries. Yoon et al. [25] indicate that ESG practices differ from developed to developing countries and even among different industries. Thus, this study provides empirical evidence from UK and Turkey, which are leading countries with different institutional contexts, regulatory frameworks, and legislations. It is contended that in developing countries with different cultural, regulatory, CG, and institutional contexts, the level and extent of voluntary compliance with CG codes can be expected to differ from that found in developed countries [27]. Hence, the current study focuses on one of the world’s fastest-growing economies, Turkey. Turkey’s unique institutional, legal, and investment settings provide an interesting case for research.

Moreover, investigating the effect of sustainability and board practices in developing countries, for which there is a dearth of empirical evidence, is crucial in acquiring a complete understanding of sustainability and environmental reforms and disclosure behavior. The comparison of the two countries is justified by the fact that each country has its unique institutional, legal, and investment settings. In addition, while sustainability and environmental reporting are mandatory in the UK [7,28], it is voluntary in Turkey [5]. Chouaibi and Affes [29] indicate that institutional and/or cultural factors influence top management’s environmental reporting behavior in terms of the quality of published information. In the same context, Holland and Foo [30] report that legal and institutional frameworks influence the level and quality of environmental information disclosure in the UK. Importantly, Husted and Sousa-Filho [31] advocate that the institutional context is important, and the unique configuration of institutional attributes may produce results that differ from those found in the mainstream literature. Jamali and Neville [32] report that CSR in an institutional framework leads to various actions and organizational strategies in order to obtain certain legitimacy and a trustworthy image. Based on this, we proposed a comparison between the UK and Turkey, which have different institutional and legal frameworks, to test whether there is a significant difference in the reporting scope of both countries. This has a practical implication for sustainability and environmental reporting in developed and developing countries.

Second, we estimate the influence of ESG on FV with the inclusion of several factors that may affect this relationship. We include some sustainability indicators such as ESG, environmental pillar, social pillar, governance pillar, sustainability expenses and incentives, the existence of an environmental team, and ESG controversies that may affect the level of ESG implementation. Further, we account for some board characteristics, including board size [7,33], independence [14,21,33,34], expertise [2,14,35] and diversity [7,14,23,33]. In addition, this relationship considers the firm size, revenue growth, and financial leverage as controls. Third, the empirical findings of the present study considerably extend the existing stock of knowledge related to accounting theories. More specifically, this study is based on voluntary disclosure theory [36,37], legitimacy theory [36,38,39,40,41], signaling theory [40,41,42], asymmetry information theory [42], and stakeholder theory [40,42]. This research provides empirical evidence to understand the variations in environmental and ESG disclosures between the two countries. Moreover, it explores the influence of ESG performance on FV over the short and long terms. It helps in understanding the nature of disclosures, the association of FV, the role of the board, and specific factors in this regard. It also provides empirical implications for ESG disclosure agencies and regulatory authorities to provide a comprehensive framework or guidelines for ESG reporting in order to achieve overall sustainability. Fourth, this study builds on prior research and adopt a comprehensive method to measure FV using three measurements: Tobin’s Q, stock prices, and market to book value.

Using a sample of 238 firms from the UK and 81 firms from Turkey for the period from 2016 to 2019, first, we estimate how the influence of ESG issues, board characteristics, and firms’ specifics on stock prices based on an overall- and country-wise sample. Similarly, in the second step, we estimated the same using market-to-book value as a measure of firms’ value. Third, an alternative analysis is offered to check the robustness of the results and provide a comprehensive finding based on different measures for firms’ value. Finally, we explore the effect of past historical sustainability performance using the lagged values of sustainability indicators, board attributes, and forms’ specifics to estimate the short- and long-term impact on firms’ value. The results provide consistent and robust outcomes across the models.

The remainder of this study is organized as follows. Section 2 introduces the literature review, and Section 3 describes the research methodology. Then, Section 4 estimates the results, and Section 5 concludes.

2. Literature Review

2.1. ESG and Firm Value

Several recent studies have investigated the relationship between ESG and FV [3,17,18,43]. A stream of research report that ESG disclosure has a positive impact on FV [2,3,18,44,45,46,47,48,49,50,51,52,53]. Another research stream investigated the relationship between ESG and FV in different contexts; stock value in Hong Kong [24], media controversies [54], debt financing in real estate investment trust [55], portfolios performance [56], several governance measures [13], ESG certification [57], green innovation [20], ESG disclosure [21], and valuation methods [58]. They all suggest that integrating ESG practices can lead to a significant and positive influence on FV. In another quest, the relationship between IPO and sustainability practices has been explored [12]. It is reported that the IPO positively affects sustainability and governance indicators in the three years following the IPO, indicating an improved image and better compliance practices.

Friske et al. [3] investigate the connection between voluntary sustainability reporting and FV (Tobin’s Q). They conclude that sustainability reporting is an expensive signal at first. Still, it boosts FV over time as corporates learn how to better communicate sustainability goals to stakeholders and investors learn how to scrutinize reports carefully. Similarly, Abdi et al. [18] argue that the cost of ESG initiatives impacts firms’ value. Fatemi et al. [59] also find a significant positive relationship between ESG score and corporates’ value. Temiz [44] concludes that there is a close link between firms’ ESG disclosure scores, FV, and firm performance. Ioannou and Serafeim [49] argue that firms with strong ESG performance have a high FV. Consistently, Li et al. [51] indicate that superior ESG disclosure increases FV by increasing stakeholder trust in the firm. Chouaibi and Chouaibi [46] investigate the relationship between social and ethical strengths and corporate value, with green innovation acting as a moderator. They show that social and ethical strengths improve corporate value, and green innovation moderators positively affect this relationship. They also find a significant positive association between societal and ethical actions and the market valuation of enterprises over time in the long run. Jin Kim et al. [60], Behl et al. [19], and Y. Li et al. [13] also agree that ESG disclosure improves FV in the long run. Patel et al. [61] indicate that investors anticipate lower short-term growth for corporations with higher ESG scores. In the same vein, Aggarwal et al. [62] investigate the long-term impact of mandatory corporate governance (CG) rule on FV. They report that firms with lower value and less shareholder-friendly governance were shown to be more vulnerable.

Nevertheless, another research stream reveals an insignificant relationship between ESG disclosure and FV [63,64,65]. For example, Fatemi et al. [59] found that ESG disclosures, per se, decrease firm valuation. Behl et al. [19] argue that it is unclear whether ESG disclosures will be considered as a cost that will lead to the destruction of FV or as a long-term investment that will return benefits in the long run. Fatemi et al. [59] also advocate that ESG investments enhance FV while weaknesses diminish it, with disclosures acting as a moderator since more disclosures mitigate the effect of weaknesses while improving the positive effects. Qureshi et al. [23] state that firms’ spending for sustainable development may diminish profitability and, thus, FV. As a result, it is critical to evaluate the effects of sustainability disclosure on business value.

2.2. ESG and the Board of Directors

Agency problems, and thus good governance, affect FV in two different ways. First, effective CG may positively influence stock prices, implying that investors expect less cash flow to be moved and a larger share of the company’s revenues to be returned to them as interest or dividends [66,67]. Second, effective governance may reduce uncertainty about future cash flows and the cost of auditing by reducing the expected return on equity, leading to lower capital costs [68]. Nevertheless, It is unclear whether effective governance is related to higher FV because the costs of developing stronger governance practices may outweigh the benefits [69,70].

Previous literature has extensively investigated the impact of CG on FV [14,70,71,72]. Previous studies document that stronger CG is linked with higher FV [73,74,75]. Much of this literature has dealt with specific aspects of CG, such as ownership or board structure and company value. For example, Armstrong et al. [76] indicate that there is a positive impact of board independence on FV. Similarly, Khaoula and Moez [14] indicate that board independence and diversity have a positive and significant effect on FV by monitoring senior management and advising managers on creating and executing the firm’s strategies. Agyemang-Mintah and Schadewitz [77] suggest that women on UK corporate boards contribute significantly to FV. Contradictory, de Andres et al. [78] indicate that board size is negatively and significantly associated with FV. However, Brick et al. [79] state that previous research on the association between board size and business value report inconsistent results.

With regard to the relationship between board attributes and sustainability performance, several studies have been conducted in this regard to investigate different quests [2,5,7,13,14,21,23,33,34,80]. For example, Aksoy et al. [33], Husted and de Sousa-Filho [31], and Mahmood et al. [81] conclude that board size and the proportion of independent board members are positively related to business sustainability performance. Board size and diversity can improve decision-making processes, including ESG disclosure decisions and strategies, by bringing several issues to board agenda [7]. More particularly, Jizi et al. [82] argue that when engaging in ESG practices, a larger board size is more efficient in terms of stakeholder representation. This is consistent with the view that a larger board may have more capable directors who can deal with various important issues, such as biodiversity, pollution, and media exposure [33]. Similarly, de Villiers et al. [83] state a strong link between higher environmental performance, greater board independence, and larger boards of US firms. In the same context, several studies also report a positive and significant relationship between board diversity and ESG performance [23,31,84,85,86,87]. Husted and de Sousa-Filho [31] indicate that women on the board can bring new perspectives to board discussions, which will improve decision-making processes. In contrast, Spitzeck [88] advocates that board commitment to corporate sustainability principles is the most determinant factor rather than board composition. Manita et al. [89] also report that there is no significant association between board diversity and ESG disclosures.

3. Methodology

3.1. Data Collection and Sampling

The data used in the present study are from Thomson Reuters Eikon (Refinitiv) database, which covers the period from 2016 to 2021. At the initial stage, the data are extracted for 429 firms from turkey and 1578 firms from the UK. This initial sample has undergone strict processing and different criteria to reach the final sample. First, we eliminated all companies that did not have data for the whole period of the study. Second, the data across each variable have been checked to meet the availability of the data across the different variables for the same company to ensure the data are available across all variables. We have deleted some companies that have missing values in some variables. This yielded a final sample of 319 companies distributed as 81 firms from Turkey and 238 from the UK.

Table 1. The Sample of the Study.

	Companies			Observations
	Turkey	UK	Total	Turkey	UK	Total
Initial sample	429	1443	1872	2574	8658	11232
Deleted due to missing values	348	1205	1553	2088	7230	9318
The final sample	81	238	319	486	1428	1914

below presents the sample of the study.

3.2. Operational Definition of Variables

Several studies have estimated firms’ value by ESG disclosure [16,18,22,44,45,46,57,59]. Similarly, some other studies [2,18,46] used environmental pillars (E, S, or G) to predict firms’ value. However, no single study includes sustainability expenses and incentives, the existence of an environmental team, and ESG controversies to investigate their impact on firms’ value. In the same respect, studies examining FV have considered several board characteristics. For example, [2,7,14,33] linked firm value with board size, [2,14,21] explored the association between board independence and firms’ value, and [7,14,23,33,46] considered the impact of board diversity on firms value. However, no study included the effect of board expertise on the relationship between ESG and firms’ value.

With regard to firms’ characteristics, several studies have considered several firms’ specifics in the relationship between ESG and firms’ value. For example, [7,14,16,18,44,45,56] used firm size as a control variable in their studies. They all agree that firm size has a positive effect on FV and the effect of ESG performance depends on firms’ size. Similarly, [33,44,45] used leverage, and [13,58,90] used revenue growth as a control variable in ESG studies. They all report that revenue growth and leverage significantly affect ESG disclosures and sustainability performance.

Against this background and motivated by prior studies, the research framework of the current study includes two categories of predictors. The first category is sustainability variables, which include ESG, environmental pillar, social pillar, governance pillar, sustainability expenses and incentives, the existence of an environmental team, and ESG controversies. The second group comprises board characteristics: board size, independence, expertise, and diversity. Finally, some control variables are included in the present study, which includes firm size, leverage, and revenue growth. These variables are regressed against FV, which is measured by several metrics that include stock price, market-to-book value, and Tobin’s Q.

Table 2. Operational definition of the variables of the study.

Variable	Symbol	Definition
Dependent Variables
Price Close	PRICE	“The latest available closing price. If there are no trades for the most recent completed tradable day, the most recent prior tradable day with trading activity is used, provided the last tradable day for the instrument is within 378 completed calendar days (54 weeks)”.
Market to book value	MTB	“The value of assets minus liabilities divided by share market price into number of outstanding shares”
Tobin’s Q	TQ	“Is the market value of equity and book value of debts divided by the book value of assets”
Independent Variables
Environmental Pillar Score	ENVP	“The environmental pillar measures a company’s impact on living and non-living natural systems, including the air, land and water, as well as complete ecosystems. It reflects how well a company uses best management practices to avoid environmental risks and capitalize on environmental opportunities in order to generate long term shareholder value”.
Social Pillar Score	SOCP	“The social pillar measures a company’s capacity to generate trust and loyalty with its workforce, customers and society, through its use of best management practices. It is a reflection of the company’s reputation and the health of its license to operate, which are key factors in determining its ability to generate long term shareholder value”
Governance Pillar Score	GOVP	“The corporate governance pillar measures a company’s systems and processes, which ensure that its board members and executives act in the best interests of its long-term shareholders. It reflects a company’s capacity, through its use of best management practices, to direct and control its rights and responsibilities through the creation of incentives, as well as checks and balances in order to generate long term shareholder value”
ESG Combined Score	ESG	“Refinitiv ESG Combined Score is an overall company score based on the reported information in the environmental, social and corporate governance pillars (ESG Score) with an ESG Controversies overlay”
Environmental Expenditures	ENVEXP	“Total amount of environmental expenditures.- All environmental investment and expenditures for environmental protection or to prevent, reduce, control environmental aspects, impacts, and hazards. It also includes disposal, treatment, sanitation, and clean-up expenditure”
ESG Controversies Score	ESGCON	“ESG controversies category score measures a company’s exposure to environmental, social and governance controversies and negative events reflected in global media”
Sustainability Compensation Incentives	SUSINC	“Is the senior executive’s compensation linked to CSR/H& S/Sustainability targets?”
Environment Management Team Score	ENVTEAM	“Does the company have an environmental management team?—in scope are any team that performs the functions dedicated to environmental issues—an individual or team at any level composed of employees, even if the name of the team is different performing implementation of the environmental strategy—it is important to understand that the members of the team include employees of the company, who are operational on a day to day basis and are not the board committees (directors)”
Board Characteristics
Board Size	BSIZE	The total number of board members at the end of the fiscal year.
Board Independent	BIND	Percentage of independent board members as reported by the company.
Board Specific Skill	BEXP	Percentage of board members who have either an industry specific background or financial background
Board Diversity	BDIV	Percentage of female on the board.
Control Variables
Firm Size	SIZE	Total assets of a firm
Leverage	LEV	Total debts divided by total assets
Revenue Growth	REVGR	The year-over-year change in revenue

provides the measurement and definition of the variables.

3.3. Econometric Tools and Model Specification

The present study utilizes panel data analysis with fixed and random effect models. At the initial stage, we estimate our analysis using a redundant fixed effects model to determine the proper analysis structure for the data. The results of this test indicate that panel data with fixed and random models are more proper for the estimation of the results of the present study. Then, we conducted a panel data analysis with fixed and random effect model choices using the Hausman Test, which yielded that the fixed effect model is the proper choice for the data set (p-value > 0.05). This is consistent with prior studies that examine the relationship between ESG and FV [6,16,18,23,34,44,45,46,60,91,92]

The results of the present study are estimated in three stages. First, we analyze the effect of sustainability indicators, board characteristics, and firm specifics on FV measured by stock market prices [23,24,25] and market-to-book value [18,22,26]. This is estimated for the overall collective sample of both countries and then estimated individually for each country. Second, we provide an alternative measure of FV using Tobin’s Q. This is to test the results across different measures of FV used by prior studies. Finally, we provide an additional test with lagged independent variables to test whether the historical ESG disclosures influence FV over the long run. To this end, the following models are framed:

$$\mathit{F}\mathit{i}\mathit{r}\mathit{m}\mathit{V}\mathit{a}\mathit{l}\mathit{u}{\mathit{e}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1}}{\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{7}} {\mathit{C}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2}}{\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{4}} {\mathit{X}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3}}{\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{3}}{\mathit{Y}}_{\mathit{i}\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$$

(1)

where ${\mathit{C}}_{\mathit{i}\mathit{t}}$ represents sustainability indicators, ${\mathit{X}}_{\mathit{i}\mathit{t}}$ indicates board characteristics, ${\mathit{Y}}_{\mathit{it}}$ refers to firm specifics, and i, t and ${\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$ measure the individual effect, the temporal effect, and the stochastic error, respectively. Where;

$$\begin{gathered} {\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{7}} {\mathit{C}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{N}\mathit{V}\mathit{P}{ }_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{S}\mathit{O}\mathit{C}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{G}\mathit{O}\mathit{V}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}} \\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{6} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{7} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}} \end{gathered}$$

(2)

$${\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{4}} {\mathit{X}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$$

(3)

$${\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{3}} {\mathit{Y}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$$

(4)

Accordingly, $\mathit{F}\mathit{i}\mathit{r}\mathit{m}\mathit{V}\mathit{a}\mathit{l}\mathit{u}\mathit{e}$ is functioned by ${\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{7}} {\mathit{C}}_{\mathit{i}\mathit{t}}$ as an indicator of sustainability indicators, ${\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{4}} {\mathit{X}}_{\mathit{i}\mathit{t}}$ as measures of board characteristics, and ${\displaystyle \sum }_{\mathit{j}=\mathbf{1}}^{\mathbf{3}} {\mathit{Y}}_{\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$ as some firm specifics. Based on these equations, the following main Model is formulated:

$$\begin{gathered} \mathit{F}\mathit{i}\mathit{r}\mathit{m}\mathit{V}\mathit{a}\mathit{l}\mathit{u}{\mathit{e}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{N}\mathit{V}\mathit{P}{ }_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{S}\mathit{O}\mathit{C}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{G}\mathit{O}\mathit{V}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{6} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}}+ \\ {\mathit{\beta }}_{\mathbf{7} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{13} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}}+ \\ {\mathit{\beta }}_{\mathbf{14} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}}+{\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{1}\right) \end{gathered}$$

Based on the measurement of firms’ value, we use two proxies that are $\mathit{P}\mathit{R}\mathit{I}\mathit{C}{\mathit{E}}_{\mathit{i}\mathit{t} }$ and $\mathit{M}\mathit{T}{\mathit{B}}_{\mathit{i}\mathit{t} }$. Accordingly, model (1) is estimated based on these two measures. The following are the estimated models:

$\begin{array}{l}\mathit{P}\mathit{R}\mathit{I}\mathit{C}{\mathit{E}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{N}\mathit{V}\mathit{P}{ }_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{S}\mathit{O}\mathit{C}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{G}\mathit{O}\mathit{V}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{6} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}}+\\ {\mathit{\beta }}_{\mathbf{7} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{13} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}}+\\ {\mathit{\beta }}_{\mathbf{14} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}}+{\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}} \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{1}\mathbf{a}\right)\end{array}$$\begin{array}{l}\mathit{M}\mathit{T}{\mathit{B}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{N}\mathit{V}\mathit{P}{ }_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{S}\mathit{O}\mathit{C}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{G}\mathit{O}\mathit{V}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{6} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}}+\\ {\mathit{\beta }}_{\mathbf{7} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{13} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}}+\\ {\mathit{\beta }}_{\mathbf{14} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}}+{\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}} \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{1}\mathbf{b}\right)\end{array}$

ESG is measured using the ESG score and the three sustainability pillars: environmental, social, and governance. Accordingly, to avoid multicollinearity issues and provide the investigation of ESG on FV based on the overall ESG score and the individual ESG pillar, we run separate models for the overall ESG score and the three ESG pillars. This is motivated by prior studies [4,22,60] that indicate that the effect of ESG on FV differs based on the individual ESG components. Thus, the following models are designed:$\begin{array}{l}\mathit{F}\mathit{i}\mathit{r}\mathit{m}\mathit{V}\mathit{a}\mathit{l}\mathit{u}{\mathit{e}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{S}\mathit{G}{ }_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{6} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+\\ {\mathit{\beta }}_{\mathbf{7} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}}+{\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{2}\right)\end{array}$ $\begin{array}{l}\mathit{P}\mathit{R}\mathit{I}\mathit{C}{\mathit{E}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{S}\mathit{G}{ }_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{6} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+\\ {\mathit{\beta }}_{\mathbf{7} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}}+{\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{2}\mathbf{a}\right)\end{array}$ $\begin{array}{l}\mathit{M}\mathit{T}{\mathit{B}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{S}\mathit{G}{ }_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{6} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+\\ {\mathit{\beta }}_{\mathbf{7} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}}+{\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{2}\mathbf{b}\right)\end{array}$

4. Analysis and Discussion

4.1. Descriptive Statistics

The results in

Table 3. Descriptive statistics.

Variables	Overall				UK				Turkey
	Max.	Min.	Mean	SD	Max.	Min.	Mean	SD	Max.	Min.	Mean	SD
$\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$	217.14	0.01	11.74	18.92	217.14	0.01	14.74	20.93	39.06	0.08	2.96	4.53
$\mathit{M}\mathit{T}\mathit{B}$	38566.74	0.16	383.20	2107.56	38566.74	0.16	494.28	2418.67	6129.70	0.63	58.03	424.37
$\mathit{T}\mathit{Q}$	3.28	0.48	1.26	0.22	3.28	0.48	1.25	0.22	2.73	0.95	1.29	0.24
$\mathit{E}\mathit{N}\mathit{V}\mathit{P}$	97.84	0.21	45.42	28.80	97.38	0.26	50.32	25.22	97.84	0.21	31.10	33.50
$\mathit{S}\mathit{O}\mathit{C}\mathit{P}$	97.74	0.28	52.31	26.92	96.73	1.93	58.34	20.46	97.74	0.28	34.64	34.71
$\mathit{G}\mathit{O}\mathit{V}\mathit{P}$	98.60	0.32	54.70	27.05	98.60	5.30	63.53	19.27	90.63	0.32	28.88	29.90
$\mathit{E}\mathit{S}\mathit{G}$	94.48	0.25	48.52	23.23	93.76	3.59	54.36	16.30	94.48	0.25	31.43	30.84
$\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$	97.22	0.11	24.70	38.48	97.22	0.21	24.88	38.55	95.66	0.11	24.38	38.42
$\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}\mathit{T}$	100.00	0.00	78.84	36.81	100.00	0.82	86.90	27.51	100.00	0.00	55.10	48.58
$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$	1.00	0.00	0.29	0.45	1.00	0.00	0.38	0.49	1.00	0.00	0.03	0.18
$\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$	92.35	0.00	43.21	39.94	92.35	0.00	45.99	39.65	91.04	0.00	35.24	39.76
$\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	19	5	9.35	2.61	19	5	9.28	2.51	17	5	8.31	2.21
$\mathit{B}\mathit{I}\mathit{N}\mathit{D}$	0.93	0.30	0.51	0.11	0.91	0.30	0.52	0.12	0.93	0.32	0.48	0.13
$\mathit{B}\mathit{E}\mathit{X}\mathit{P}$	0.72	0.21	0.48	0.14	0.72	0.25	0.49	0.13	0.68	0.21	0.48	0.11
$\mathit{B}\mathit{D}\mathit{I}\mathit{V}$	0.83	0.25	0.45	0.13	0.80	0.30	0.45	0.11	0.83	0.25	0.42	0.15
$\mathit{L}\mathit{E}\mathit{V}$	328.80	-0.01	1.65	9.42	328.80	-0.01	1.53	9.41	202.39	0.00	1.98	9.46
$\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	28.72	16.10	22.05	1.88	28.72	17.71	22.27	1.89	25.47	16.10	21.40	1.73
$\mathit{R}\mathit{E}\mathit{V}\mathit{G}\mathit{R}$	26.69	9.60	21.25	1.83	26.69	9.60	21.35	1.85	24.45	15.82	20.83	1.63

Notes: Number of observations are 1914. Figure for $\mathit{I}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$, $\mathit{E}\mathit{M}\mathit{M}\mathit{R}\mathit{E}\mathit{V}$, $\mathit{F}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$, $\mathit{M}\mathit{C}\mathit{A}\mathit{P}$, $\mathit{R}\mathit{E}\mathit{V}\mathit{G}\mathit{R}$, and $\mathit{P}\mathit{R}\mathit{O}\mathit{F}$ are in billions. All variables are defined in Table 2.

provide descriptive statistics in the form of minimum, maximum, mean, and standard deviation (SD). The minimum values of the variables reflect the lowest score or value for each variable; however, the maximum values indicate the highest score or value for the variable across the sampled companies. Further, the SD values show the variation of the values of each variable from their mean values. The results show that there is a variation in the mean values of $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$, $\mathit{M}\mathit{T}\mathit{B}$, and $\mathit{T}\mathit{Q}$ between the UK and Turkey. With regards to sustainability indicators, the results indicate that $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$, $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$, $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$, $\mathit{E}\mathit{S}\mathit{G}$, $\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}\mathit{N}\mathit{T}$, $\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$, and $\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$ in the UK exhibit higher score mean values than Turkey. This indicates that sustainability indicators in the UK demonstrate better performance than in Turkey. However, both UK and Turkey have similar average amounts of $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$. Further, the results show that board size in the UK varies between a minimum of 5 and a maximum of 19 board members, with an average size of 9 members. The UK firms have average board independence of 52%, an average of 49% being expertise members, and 45% of the board members are females. In contrast, Turkish firms have a minimum board size of 5, a maximum of 17, and on average of 8 board members, with 48% being independent and expertise board members and 42% being females. Finally, leverage, firm size, and revenue growth have average values of 1.65, 22.27, and 21.35 for UK firms and 1.98, 21.40, and 20.83 for Turkish firms, respectively.

4.2. Correlation Analysis

The results in

Table 4. Correlation Matrix.

Probability	$\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$	$\mathit{M}\mathit{T}\mathit{B}$	$\mathit{T}\mathit{Q}$	$\mathit{E}\mathit{N}\mathit{V}\mathit{P}$	$\mathit{S}\mathit{O}\mathit{C}\mathit{P}$	$\mathit{G}\mathit{O}\mathit{V}\mathit{P}$	$\mathit{E}\mathit{S}\mathit{G}$	$\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$	$\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}\mathit{T}$	$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$	$\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$	$\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	$\mathit{B}\mathit{I}\mathit{N}\mathit{D}$	$\mathit{B}\mathit{E}\mathit{X}\mathit{P}$	$\mathit{B}\mathit{D}\mathit{I}\mathit{V}$	$\mathit{L}\mathit{E}\mathit{V}$	$\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	$\mathit{R}\mathit{E}\mathit{V}\mathit{G}\mathit{R}$
$\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$	1
$\mathit{M}\mathit{T}\mathit{B}$	0.09 ***	1.00
$\mathit{T}\mathit{Q}$	0.03	−0.10 ***	1.00
$\mathit{E}\mathit{N}\mathit{V}\mathit{P}$	0.22 ***	0.04	0.03 *	1.00
$\mathit{S}\mathit{O}\mathit{C}\mathit{P}$	0.23 ***	−0.01 **	0.06 **	0.61 ***	1.00
$\mathit{G}\mathit{O}\mathit{V}\mathit{P}$	0.20 ***	0.06 **	−0.07 ***	0.57 ***	0.67 ***	1.00
$\mathit{E}\mathit{S}\mathit{G}$	0.24 ***	0.05 **	0.00	0.82 ***	0.88 ***	0.79 ***	1.00
$\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$	0.04 ***	0.04 **	0.08 ***	0.45 ***	0.38 ***	0.28 ***	0.39 ***	1.00
$\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}\mathit{T}$	0.12 **	0.05 **	−0.04 *	0.29 ***	0.37 ***	0.44 ***	0.61 ***	0.12 ***	1.00
$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$	0.13 ***	0.03	0.03	0.23 ***	0.26 ***	0.41 ***	0.30 ***	0.12 ***	0.11 ***	1.00
$\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$	0.05 **	0.08 ***	0.04	0.55 ***	0.49 ***	0.38 ***	0.49 ***	0.31 ***	0.20 ***	0.11 ***	1.00
$\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	0.08 ***	0.01	0.03	0.29 ***	0.39 ***	0.52 ***	0.48 ***	0.11 ***	0.47 ***	0.19 ***	0.22 ***	1.00
$\mathit{B}\mathit{I}\mathit{N}\mathit{D}$	0.12 ***	0.08 ***	−0.05 **	0.43 ***	0.49 ***	0.62 ***	0.52 ***	0.15 ***	0.31 ***	0.20 ***	0.25 ***	0.32 ***	1.00
$\mathit{B}\mathit{E}\mathit{X}\mathit{P}$	0.05 **	0.06 **	−0.14 ***	0.16 ***	0.24 ***	0.34 ***	0.29 ***	0.04 **	0.36 ***	0.09 ***	0.14 ***	0.21 ***	0.16 ***	1.00
$\mathit{B}\mathit{D}\mathit{I}\mathit{V}$	0.18 ***	0.08 ***	−0.04	0.47 ***	0.54 ***	0.59 ***	0.57 ***	0.16 ***	0.33 ***	0.20 ***	0.30 ***	0.39 ***	0.49 ***	0.18 ***	1.00
$\mathit{L}\mathit{E}\mathit{V}$	−0.03	−0.01	0.13 ***	0.01	0.02	0.01 ***	0.01 ***	0.01	0.06	0.02	0.05 **	0.02	0.02	−0.01	0.01	1.00
$\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	0.21 ***	−0.01	−0.03	0.55 ***	0.52 ***	0.40 ***	0.42 ***	0.29 ***	−0.05 **	0.17 ***	0.33 ***	0.06 **	0.36 ***	0.15 ***	0.36 ***	−0.01	1.00
$\mathit{R}\mathit{E}\mathit{V}\mathit{G}\mathit{R}$	0.20 ***	−0.05 **	0.09 ***	0.44 ***	0.45 ***	0.36 ***	0.37 ***	0.26 ***	−0.06 **	0.16 ***	0.28 ***	0.07 ***	0.27 ***	0.06 **	0.32 ***	0.02	0.62 ***	1.00

Note: ***, **, and * indicate a significance level at 0.01, 0.05, and 0.10, respectively. All variables are defined in Table 2.

provide a correlation analysis for the study’s variables. The results show that all independent variables exhibit positive and significant associations with all three dependent variables: $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$, $\mathit{M}\mathit{T}\mathit{B}$, and $\mathit{T}\mathit{Q}$. On the one hand, the significance level for these correlations varies across the relationships between an independent and dependent variable or between two predictors from the other. However, all independent variables have significant positive correlations (p-values < 0.01) with the dependent variables. This indicates one direct positive and significant relationship between ESG, environmental performance, and firms’ specifics with firms’ value. Further, the results show that the highest correlation value is 0.62, which is found between $\mathit{G}\mathit{R}\mathit{O}\mathit{W}\mathit{T}\mathit{H}$ and $\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$. This implies that there are no multicollinearity concerns exist in the current study, and all other correlation values are less than this value, indicating the absence of multicollinearity issues. $\mathit{E}\mathit{S}\mathit{G}$ has correlation values of 0.82, 0.88, and 0.79 with $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$, $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$, and $\mathit{G}\mathit{O}\mathit{V}\mathit{P}$, respectively. However, we estimated $\mathit{E}\mathit{S}\mathit{G}$ in a separate model from its three pillars to avoid multicollinearity issues.

4.3. Results Estimation

4.3.1. The Impact of ESG, Board Characteristics, and Firms’ Specifics on Firms’ Value

Table 5. Impact of ESG, Board Characteristics, and Firms’ Specifics on Firms Value- Overall Model.

Variables	Firms’ Value: Stock Prices		Firms’ Value: Market to Book Value
	DV = PRICE		DV = MTB
	ESG Pillars Model	Overall ESG Model	ESG Pillars Model	Overall ESG Model
$\mathit{C}$	−16.6330 ***	−14.8414 ***	−123.8793	−79.8879
	3.6888	3.8730	177.3359	165.0396
	−4.5090	−3.8320	−0.6986	−0.4841
$\mathit{E}\mathit{N}\mathit{V}\mathit{P}$	0.0038 **		0.6595 ***
	2.1060		16.6840
$\mathit{S}\mathit{O}\mathit{C}\mathit{P}$	0.0063 **		0.3479 ***
	2.3760		4.0265
$\mathit{G}\mathit{O}\mathit{V}\mathit{P}$	0.0003		0.0064
	0.1033		0.1010
$\mathit{E}\mathit{S}\mathit{G}$		0.0156 ***		0.4490 ***
		4.6176		4.2575
$\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$	0.0041 ***	0.0043 ***	0.4674 ***	0.5096 ***
	3.7616	4.2089	3.9111	4.9639
$\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$	−0.0061 ***	−0.0037 **	−0.2413 ***	−0.3307 ***
	−3.6492	−2.4236	−4.5196	−5.4009
$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$	0.0214	0.0071	11.5508 ***	8.2405 *
	0.1399	0.0450	2.6719	1.6910
$\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$	0.0033 *	0.0046 ***	0.1593 *	0.1777 **
	1.6039	3.6669	1.7024	1.8951
$\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	−0.0003	−0.0037 **	−0.0885 ***	−0.1722 ***
	−0.2624	−2.0576	−2.6453	−4.3613
$\mathit{B}\mathit{I}\mathit{N}\mathit{D}$	1.7010 ***	1.6684 ***	28.7184 ***	26.0217 ***
	8.8395	8.3738	3.6754	3.3103
$\mathit{B}\mathit{E}\mathit{X}\mathit{P}$	0.0010	0.0015	0.2123 ***	0.1667 ***
	0.8754	1.2554	6.2320	5.0080
$\mathit{B}\mathit{D}\mathit{I}\mathit{V}$	0.0040 ***	0.0040 ***	0.1890 ***	0.1671 **
	3.1439	3.5484	2.9367	2.7635
$\mathit{L}\mathit{E}\mathit{V}$	−0.0060 *	−0.0055 *	−0.1839 **	−0.1495 *
	−1.4956	−1.6193	−2.4561	−1.6619
$\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	−0.0015	−0.0017 *	−0.1335 ***	−0.1121 ***
	−1.0685	−1.6323	−4.4879	−7.6196
$\mathit{R}\mathit{G}\mathit{R}\mathit{W}$	0.4456 **	0.4975 ***	6.9703 ***	6.1209 ***
	5.7876	5.0360	5.7307	4.4703
YEAR CONTROLS	YES	YES	YES	YES
$\mathit{I}\mathit{N}\mathit{D}\mathit{U}\mathit{S}\mathit{T}\mathit{R}\mathit{Y} \mathit{C}\mathit{O}\mathit{N}\mathit{T}\mathit{R}\mathit{O}\mathit{L}\mathit{S}$	YES	YES	YES	YES
R-squared	0.9436	0.9431	0.9320	0.9308
Adjusted R-squared	0.9317	0.9311	0.9177	0.9163
F-statistic	79.4133	79.1872	65.0675	64.3059
Prob(F-statistic)	0.0000	0.0000	0.0000	0.0000
Durbin–Watson stat	1.7067	1.6624	1.4226	1.4510

Note: ***, **, and * indicate a significance level at 0.01, 0.05, and 0.10, respectively. All variables are defined in Table 2.

estimates the impact of ESG pillars, ESG, other sustainability determinants, board characteristics, and firms’ specific value. The results in Model (1a) indicate that among ESG pillars, $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$ and $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$ exhibit a positive and significant impact on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ at the level of 5% (p-Value < 0.05); however, $\mathit{G}\mathit{O}\mathit{V}\mathit{P}$ shows an insignificant influence on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$. This indicates a positive association between $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$, $\mathit{G}\mathit{O}\mathit{V}\mathit{P}, \mathit{and} \mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E},$ and both $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$ and $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$ contribute positively to the firm’s values measured by $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$. The results in Model (1b) also demonstrate that $\mathit{E}\mathit{S}\mathit{G}$ has a positive and significant influence on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ (p-Value < 0.01), meaning that there is a significant association between $\mathit{E}\mathit{S}\mathit{G}$ and $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$. Similarly, the results reveal that $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$, $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$, and $\mathit{E}\mathit{S}\mathit{G}$ exhibit a significant positive effect (p-Value < 0.01) on $\mathit{M}\mathit{T}\mathit{B}$. This is in line with Abdi et al. [18], who indicate that a greater level of financial efficiency positively and considerably rewards a firm’s involvement in social and environmental activities. However, this makes it difficult to deliver competitive outcomes while integrating ESG indicators to positively influence business value and ensure a decent public reputation [33].

With regards to a country-wide effect,

Table 6. Impact of ESG, Board Characteristics, and Firms’ Specifics on Firms Value- Country-wide Model.

Variables	Firms’ Value: Stock Prices				Firms’ Value: Market to Book Value
	DV = PRICE				DV = MTB
	ESG Pillars Model		Overall ESG Model		ESG Pillars Model		Overall ESG Model
	UK	Turkey	UK	Turkey	UK	Turkey	UK	Turkey
$\mathit{C}$	−47.7116 ***	−13.9540 ***	−50.1469 ***	−14.7975 ***	−419.4291 ***	110.2917 ***	−953.0930 ***	−62.0940 ***
	−4.4740	−7.2985	−6.2687	−6.5423	−2.4047	3.2924	−2.5908	−0.5477
$\mathit{E}\mathit{N}\mathit{V}\mathit{P}$	0.0200 ***	0.0002			1.5391 ***	−0.0556
	4.2260	0.1723			4.6289	−1.4828
$\mathit{S}\mathit{O}\mathit{C}\mathit{P}$	0.0326 ***	0.0068 ***			0.2855 ***	0.2898 ***
	2.9761	6.7943			7.9470	6.7813
$\mathit{G}\mathit{O}\mathit{V}\mathit{P}$	0.0033	−0.0077 ***			0.1824 *	−0.0922 **
	0.5497	−4.5776			1.7076	−2.3112
$\mathit{E}\mathit{S}\mathit{G}$			0.0434 ***	−0.0002			1.2353 ***	0.2921 ***
			4.2907	−0.1309			6.7408	7.0951
$\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$	0.0146 ***	−0.0009	0.0145 ***	−0.0015 **	0.5144 **	−0.0094 *	0.3370 **	−0.0046
	4.3163	−0.6903	3.5107	−2.8026	2.2211	−1.5507	2.5049	−0.2188
$\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$	0.0032 **	0.0002	0.0037	0.0006	0.0621 ***	−0.0440 ***	0.3141 ***	−0.0559 ***
	1.8716	0.0570	1.3722	0.6189	2.3053	−4.8497	6.1266	−5.4372
$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$	0.1239	0.0760	0.0909	0.0384	4.3043	−3.3919 **	7.5279 ***	−3.3287 ***
	0.5667	0.5553	0.5396	0.4177	0.8930	−2.2332	2.8386	−6.6526
$\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$	0.0071 ***	0.0039 ***	0.0060 ***	0.0027 ***	0.2321 ***	0.0545 **	0.2826 **	0.0595 ***
	5.6837	2.7325	3.8791	9.5848	2.1498	1.9847	2.4799	2.9882
$\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	−0.0010	−0.0019 ***	−0.0006	−0.0020 ***	0.0032	−0.0808 ***	0.1189	0.0433 **
	−0.3721	−3.2353	−0.2469	−2.9021	0.0482	−5.2854	1.1577	2.3488
$\mathit{B}\mathit{I}\mathit{N}\mathit{D}$	0.0018 ***	0.0025 ***	0.0020 ***	0.0016 ***	0.0849 **	0.0165 **	0.3215 **	0.0124 **
	5.7427	6.2025	−0.8851	2.9259	0.7084	0.6894	3.3445	0.9718
$\mathit{B}\mathit{E}\mathit{X}\mathit{P}$	0.0043 ***	0.0028 ***	0.0029 **	0.0037 ***	0.2873 **	0.0090	0.2069 **	0.0275
	3.4655	2.8367	1.7010	2.7700	2.3489	−0.6393	2.5401	−1.3318
$\mathit{B}\mathit{D}\mathit{I}\mathit{V}$	0.0054 ***	0.0024 ***	0.0073 ***	0.0014 ***	0.1408 **	0.0615 **	0.3014 **	0.0705 **
	1.4062	4.0181	2.5212	3.9185	2.9529	2.4277	2.0502	2.5288
$\mathit{L}\mathit{E}\mathit{V}$	−0.0079 **	0.0053 ***	−0.0059 **	0.0049 ***	−0.1384 *	0.0278 **	0.2754 **	0.0371
	−1.9513	7.2831	−2.1316	4.1960	−1.8347	2.2490	−1.9176	1.2314
$\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	1.5700 ***	0.5675 ***	1.6352 ***	0.6216 ***	12.9849 *	1.8509	37.6841 **	9.1818 **
	3.3276	5.3948	4.6308	5.2337	1.6683	0.2867	2.5187	2.1178
$\mathit{R}\mathit{G}\mathit{R}\mathit{W}$	1.0994 ***	0.2246 **	1.2131 ***	0.2081 **	24.3008 ***	2.3947 *	24.6992 ***	3.6142 ***
	4.2957	1.9506	4.9692	2.1725	3.7390	1.5488	3.5055	2.8873
YEAR CONTROLS	YES	YES	YES	YES	YES	YES	YES	YES
R-squared	0.9405	0.8954	0.9419	0.8954	0.9560	0.7592	0.9210	0.7545
Adjusted R-squared	0.9278	0.8636	0.9295	0.8646	0.9441	0.6862	0.9043	0.6822
F-statistic	73.7801	28.2161	76.3389	29.0410	80.6979	10.3966	54.9325	10.4245
Prob(F-statistic)	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Durbin–Watson stat	1.6871	2.0471	1.7140	1.9954	1.6828		1.4689	1.2452

Note: ***, **, and * indicate a significance level at 0.01, 0.05, and 0.10, respectively. All variables are defined in Table 2.

shows an estimation of the results based on the segregation of the sample into country-wide samples for UK and Turkish firms. The results show that $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$ has a significant positive impact on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ the case of UK firms; however, it exhibits an insignificant positive effect in the case of Turkish firms. The findings also reveal that $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$ has a significant and positive effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ in both cases of UK and Turkish firms. This indicates that both the environmental pillar and social issues have a significant positive association with $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B};$ however, the effect of the environmental pillar on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ in the case of Turkish firms is still limited and insignificant. Further, the results show that $\mathit{G}\mathit{O}\mathit{V}\mathit{P}$ has a statistically insignificant positive effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E},$ but it has a significant positive influence (p-Value < 0.10) on $\mathit{M}\mathit{T}\mathit{B}$ in the case of UK firms. However, it exhibits a significant but negative impact in the case of Turkish firms. This indicates that $\mathit{G}\mathit{O}\mathit{V}\mathit{P}$ exhibits a better significant association with $\mathit{M}\mathit{T}\mathit{B}$ rather than $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ in the case of UK firms, $\mathit{G}\mathit{O}\mathit{V}\mathit{P}$ have a negative relationship with the $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ of Turkish firms. In the same context, the results reveal that $\mathit{E}\mathit{S}\mathit{G}$ exhibits a significant and positive influence on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ in the case of UK firms. For Turkish firms, it demonstrates a statistically insignificant and negative effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E},$ but it has a significant positive influence on $\mathit{M}\mathit{T}\mathit{B}$. This indicates that stock prices react to the overall $\mathit{E}\mathit{S}\mathit{G}$ issues as well as the individual $\mathit{E}\mathit{S}\mathit{G}$ pillars in the case of UK firms. Comparatively, $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ reacts greatly and significantly with the individual $\mathit{E}\mathit{S}\mathit{G}$ pillars rather than the overall $\mathit{E}\mathit{S}\mathit{G}$ issues in the case of Turkish firms. This signifies that ESG issues associate with long-run firm valuation rather than the short-term stock reaction as they exhibit a higher association with $\mathit{M}\mathit{T}\mathit{B}$ rather than $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$. This is consistent with [16,18,23,33,51,56,57,59,60] who conclude that environmental, social responsibility, and CGare three subcategories of ESG have varying effects on business value; however, the overall ESG score increases business value.

The results in Table 5 reveal that $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ exhibits a significant and positive (β+) influence on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ (p-Value < 0.01) in ESG pillars and ESG models. In the same vein, $\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$ in the collective sample has a significant influence on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ at the level of 10% (p-Value < 0.10) for ESG pillars and at the level of 1% (p-Value < 0.01) for the overall ESG model. This means that the greater amounts of $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ and the existence of $\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$ associate positively and significantly with stock prices and the firm’s value. In the same respect, the results in Table 6 demonstrate that the existence of $\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$ in the companies associates significantly and positively with $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S}$ and $\mathit{M}\mathit{T}\mathit{B}$ in both cases, both the UK and Turkish firms. This could be due to the fact that companies would signal to investors and stakeholders that they have $\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$ to manage sustainability issues which, which positively and significantly affects the FV of the UK and Turkish firms.

The results in Table 6 also show that $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ have a significant and positive effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ of UK firms in both cases; the individual and the overall $\mathit{E}\mathit{S}\mathit{G}$ models. In the case of Turkish firms, $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ exhibit a statistically insignificant effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ but it has a significant negative influence on $\mathit{M}\mathit{T}\mathit{B}$ for the individual $\mathit{E}\mathit{S}\mathit{G}$ Model. However, it exhibits a statistically and negatively significant effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E},$ but it has an insignificant influence on $\mathit{M}\mathit{T}\mathit{B}$ in the case of the overall $\mathit{E}\mathit{S}\mathit{G}$ Model. This indicates that stock prices react positively and significantly with a higher level of $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ in the case of UK firms. Contradictory, the results indicate that $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ does not associate significantly with the $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$, which in turn affects negatively and adversely $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S}$ of Turkish firms. The results also confirm that the association between ESG issues and market valuation is shaped in the long run—the overall market-to-book value- rather than the short run (stock prices reaction). This is consistent with Pereira da Silva [35], who reports that investors may have low-cost information through ESG disclosures. Thus, ESG disclosures improve investors’ ability to monitor management’s behavior. However, this contradicts Friske et al. [3], who state that there is still a chance that the costs of reporting will deplete shareholder wealth while offering no major advantages to other stakeholder groups. Krüger [93] also indicates that a firm may thrive to provide non-financial disclosure to stakeholders, possibly considered a less meaningful advantage, leading to a decline in FV. Friske et al. [3] argue that sustainability reporting does not appear to be a value-added activity; investors may wonder why managers are devoting resources to it.

The results in Table 5 also show that $\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$ has a significant influence on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ (p-Value < 0.01) for ESG pillars and the overall ESG model (p-Value < 0.05). $\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$ also demonstrates a significant influence on $\mathit{M}\mathit{T}\mathit{B}$ (p-Value < 0.01) in both cases; ESG pillars and ESG models. This indicates that media controversies linked to the environmental impact of firms’ operations on natural resources or local communities negatively affect shareholders’ perceptions, which is reflected in market value.

The results in Table 6 reveal that $\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$ associates positively and significantly with $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ for individual ESG pillar model of UK firms; however, it exhibits an insignificant influence in all other models. In the case of $\mathit{M}\mathit{T}\mathit{B}$, the results demonstrate that $\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$ has a significant and positive influence on $\mathit{M}\mathit{T}\mathit{B}$ in the UK, but it has a significant and negative impact on $\mathit{M}\mathit{T}\mathit{B}$ in Turkey. This implies that $\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$ raised by media is associated positively with the market valuation of UK firms but negatively for Turkish firms. These findings are consistent with Luo et al. [94], Luo et al. [94], and Wong and Zhang [42] indicate that negative business behavior on ESG issues disclosed in the media might negatively influence firm valuation.

The results in Table 5 show that $\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$ exhibits an insignificant but positive influence on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ in both cases, ESG pillars and ESG models (p-Value > 0.10). However, it shows a significant positive effect (p-Value < 0.01) on $\mathit{M}\mathit{T}\mathit{B}$ for the ESG pillars model and the overall ESG model (p-Value < 0.10). This signifies that sustainability incentives paid to the board of directors and executives are associated positively with stock prices. However, the impact of these incentives is still limited. This could be due to the variations among countries or firm specifics. Table 6 also demonstrates that $\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$ exhibits an insignificant effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S}$ across all models in the UK and Turkey; however, it shows a positive influence on $\mathit{M}\mathit{T}\mathit{B}$ of UK firms, and it is negative for Turkish firms. The results align with the view that management incentives and activities frequently prioritize short-term gains, which are typically tied to CEO compensation, over long-term sustainable performance [80]. This is also in line with Kim et al. [60], Sun et al. [95], and Friske et al. [3]. Sun et al. [95] indicate that managers are incentivized to voluntarily disclose environmental information to attract existing or potential investors and improve their company’s corporate image. Similarly, Kim et al. [60] find that managers’ motivations to be ethical, honest, and trustworthy drive social and environmental disclosure actions.

With regards to the effect of board characteristics in the collective sample, the results show that $\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$ demonstrates an insignificant influence in the case of ESG pillar (p-Value > 0.10), it has a significant influence (p-Value < 0.05) in the case of ESG model. It also shows a significant effect (p-Value < 0.01) on $\mathit{M}\mathit{T}\mathit{B}$ in both cases. This implies that larger $\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$ associates negatively with $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$. Further, the results indicate that the effect of $\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$ on FV matters in the case of the overall sustainability performance rather than ESG pillars. Further, it reveals a higher association with the total market-to-book value rather than stock price reaction. Concerning the effect of board characteristics in the country-wide samples, the results show that $\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$ exhibit an insignificant negative effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S}$ but it is significant and positive in the case of $\mathit{M}\mathit{T}\mathit{B}$ for UK firms. For Turkish firms, it shows a statistical and negative effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S};$ however, it demonstrates a significant and negative influence on $\mathit{M}\mathit{T}\mathit{B}$ in the ESG pillars model, but it shos a significant and positive impact on $\mathit{M}\mathit{T}\mathit{B}$ in the case of the ESG model. This is consistent with Hussain et al. (2018), who report that prior studies reveal that firm’s sustainability initiatives have an insignificant association with board size. De Andres et al. [78] also conclude a negative link between FV and board size. Khaoula and Moez [14] indicate that a small board size is more effective. Similarly, Disli et al. [96] reveal that board size is negatively linked with FV; however, they advocate that good governance demonstrates societal and environmental responsibility, which leads to better operational performance and market valuation. Similarly, Husnaint and Basuki [97] indicate that despite the fact that board size has no effect on aggregate sustainability measures (ESG score, ESG controversies, and ESG combination score), there is a negative relationship between board size and governance performance.

The results in Table 5 also reveal that both $\mathit{B}\mathit{I}\mathit{N}\mathit{D}$ and $\mathit{B}\mathit{D}\mathit{I}\mathit{V}$ have a statistically significant effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ at the level of 1% (p-Value < 0.01) in both cases, ESG pillars, and ESG models except for ESG model in the case of $\mathit{M}\mathit{T}\mathit{B}$, which shows an effect at the level of 5%. This signifies that both $\mathit{B}\mathit{I}\mathit{N}\mathit{D}$ and $\mathit{B}\mathit{D}\mathit{I}\mathit{V}$ associate positively with ESG and sustainability issues which contribute positively to the firm’s value. Further, the results in Table 6 also show that $\mathit{B}\mathit{I}\mathit{N}\mathit{D}$, $\mathit{B}\mathit{E}\mathit{X}\mathit{P}$, and $\mathit{B}\mathit{D}\mathit{I}\mathit{V}$ have a significant and positive effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S}$ and $\mathit{M}\mathit{T}\mathit{B}$ in both cases of UK and Turkish firms across the all conducted models. However, $\mathit{B}\mathit{E}\mathit{X}\mathit{P}$ exhibits an insignificant and positive influence on $\mathit{M}\mathit{T}\mathit{B}$ for the overall ESG model. This means that there is a positive and significant association between a higher proportion of independent members, expertise, and diversity in the board with $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S}$ and $\mathit{M}\mathit{T}\mathit{B}$. In the same quest, $\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$ exhibits a limited effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S}$ and $\mathit{M}\mathit{T}\mathit{B}$. This indicates that a smaller number of board members associate positively with firms’ value. The results also show that $\mathit{B}\mathit{E}\mathit{X}\mathit{P}$ exhibits an insignificant influence on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$. This indicates that $\mathit{B}\mathit{E}\mathit{X}\mathit{P}$ does not associate with ESG and sustainability issues that contribute to firms’ value. In contrast, it indicates a significant positive association with $\mathit{M}\mathit{T}\mathit{B}$ at the level of 1%.

This is in agreement with Ammer et al. [21], who found that environmental sustainability practices have a positive and significant effect on business value. They believe that the inclusion of independent directors on boards promotes increased responsibility and openness, and stakeholder trust. Contradictory, Harjoto and Jo [48] indicate that when compared to CSR efforts, board independence plays a relatively minor influence in increasing business value. Regarding board diversity, our results agree with Qureshi et al. [23], who provided evidence of a positive impact of sustainability disclosure and board gender diversity on FV, implying that better management practices, increased stakeholder trust, and female presence on boards enhance firm valuation. Similarly, Khaoula and Moez [14] indicate that board diversity may also act as a positive signal to the environment or stakeholders to improve the organization’s reputation. Siagian et al. [98] also report a positive link between CG and FV. Qureshi et al. [23] report that sustainability disclosure and board gender diversity positively impact FV. They also find that corporations with more female representation on their boards perform much better in ESG performance. Consistently, Kolsi and Attayah [38] advocate that larger firms are anticipated to have more financial and expertise human resources, as well as experience, to compile, evaluate, and report ESG issues than smaller ones. Provasi and Harasheh [8] discover that female representation on both boards has almost no effect on financial performance; however, a significant association is discovered with corporate sustainability performance.

Concerning firms’ specifics, the results in Table 5 indicate that $\mathit{L}\mathit{E}\mathit{V}$ and $\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$ associate negatively with $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ in both cases, ESG pillars and the overall ESG models except for $\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$ in the case of ESG pillars model, which exhibits an insignificant effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ (p-Value > 0.05). However, it exhibits a higher effect in the case of the overall ESG model. This implies that FV has better sustainability performance in small-size companies and firms with low leverage. The results in Table 6 also find that $\mathit{R}\mathit{G}\mathit{R}\mathit{W}$ has a statistically significant effect (p-Value < 0.05) in the case of ESG pillars on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and at the level of 1% (p-Value < 0.01) on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ in all other cases. Finally, firms’ specific demonstrate a significant effect on $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}\mathit{S}$ and $\mathit{M}\mathit{T}\mathit{B}$ in the case of UK and Turkish firms across all conducted models. While firms’ $\mathit{L}\mathit{E}\mathit{V}$ shows a significant negative effect for UK firms, it has a significant and positive impact in the case of Turkish firms. This indicates that the FV of UK firms associates positively with low $\mathit{L}\mathit{E}\mathit{V}$; however, there is a positive association between higher $\mathit{L}\mathit{E}\mathit{V}$ and FV in the case of Turkish firms. Both $\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$ and $\mathit{R}\mathit{E}\mathit{V}\mathit{G}\mathit{R}$ associate positively and significantly with the FV of UK and Turkish firms across all models.

4.3.2. Additional Analysis

Alternative Measure of Firms’ Value

As far as the $\mathit{T}\mathit{Q}$ models are concerned, the results show that $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$, $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$, and $\mathit{E}\mathit{S}\mathit{G}$ exhibit better effects compared to $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ models as appear in

Table 7. Impact of ESG, Board Characteristics, and Firms’ Specifics on Firms Value- Alternative Analysis.

Variable	Firms’ Value: Tobins’Q
	ESG Pillars Model			Overall ESG Model
	Collective Sample	UK	Turkey	Collective Sample	UK	Turkey
$\mathit{C}$	1.4552 ***	1.1248 ***	0.6244 ***	1.4481 ***	1.1602 ***	0.9782 ***
	36.8032	17.7794	8.9941	34.1545	17.2089	10.5384
$\mathit{E}\mathit{N}\mathit{V}\mathit{P}$	−0.0001 *	0.0003 **	−0.0013 **
	−0.9688	1.9078	−1.9861
$\mathit{S}\mathit{O}\mathit{C}\mathit{P}$	0.0001 **	0.0017 ***	0.0014 ***
	2.3327	9.7618	4.8755
$\mathit{G}\mathit{O}\mathit{V}\mathit{P}$	0.0002 ***	0.0014 ***	0.0004 **
	3.0595	7.1599	1.9084
$\mathit{E}\mathit{S}\mathit{G}$				0.0003 ***	0.0009 ***	0.0009 ***
				6.5770	6.6520	8.3597
$\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$	0.0002 ***	0.0005 ***	−0.0003 **	0.0002 ***	0.0003 ***	−0.0002 **
	6.6431	7.0607	−2.1410	7.5086	5.9815	−2.0956
$\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$	−0.0001 ***	0.0004 **	−0.0005 ***	−0.0002 ***	0.0004 **	−0.0005 *
	−4.2482	2.0507	−4.0916	−6.6188	3.0660	−1.7557
$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$	0.0011	−0.0001 **	−0.0004	0.0000	0.0000 ***	0.0001
	−4.5356	−2.0265	−1.1687	−0.9066	−0.7735	0.6714
$\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$	0.0061 ***	0.0256 ***	0.0175 ***	0.0061 ***	0.0101 **	0.0213 ***
	4.2113	8.5537	2.9115	4.4585	1.8958	3.3522
$\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	0.0003	0.0007 ***	0.0003	0.0005	0.0006 ***	−0.0002
	6.5348	9.9128	1.2906	7.0942	10.4820	−1.4375
$\mathit{B}\mathit{I}\mathit{N}\mathit{D}$	0.0001 ***	0.0001 ***	0.0002 ***	0.0001 ***	0.0004 ***	0.0001 **
	4.4275	0.8105	1.2001	5.7403	5.1951	0.4940
$\mathit{B}\mathit{E}\mathit{X}\mathit{P}$	0.0002 ***	0.0005 ***	0.0001 **	0.0002 ***	0.0006 ***	0.0004 ***
	10.6462	5.1985	4.2832	10.8779	7.1186	5.2077
$\mathit{B}\mathit{D}\mathit{I}\mathit{V}$	0.0005 **	0.0002 ***	−0.0002	0.0007 **	0.0001 **	−0.0004 **
	6.3615	4.4505	−0.8436	10.0539	1.0836	−1.9830
$\mathit{L}\mathit{E}\mathit{V}$	0.0012 ***	0.0070 **	−0.0004	0.0013 ***	0.0072 **	−0.0004 ***
	4.5215	1.9825	−1.3848	4.1838	2.0826	−8.6349
$\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$	−0.0157 ***	0.0098 ***	0.0361 ***	−0.0160 ***	0.0108 ***	0.0579 ***
	−11.7241	4.1606	18.8575	−11.4725	4.6043	12.7982
$\mathit{R}\mathit{G}\mathit{R}\mathit{W}$	0.0067 ***	0.0115 ***	0.0327 ***	0.0075 ***	0.0125 ***	0.0747 ***
	8.0238	6.5435	9.1278	7.8063	8.0510	15.2119
$\mathit{Y}\mathit{E}\mathit{A}\mathit{R} \mathit{C}\mathit{O}\mathit{N}\mathit{T}\mathit{R}\mathit{O}\mathit{L}\mathit{S}$	YES	YES	YES	YES	YES	YES
R-squared	0.9810	0.2587	0.2866	0.9808	0.2212	0.4454
Adjusted R-squared	0.9770	0.2513	0.2610	0.9768	0.2145	0.4284
F-statistic	245.4892	35.0996	11.1924	244.8151	33.3679	26.2296
Prob(F-statistic)	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
Durbin–Watson stat	1.6259	0.4028	0.4551	1.6235	0.3973	0.4901

Note: ***, **, and * indicate a significance level at 0.01, 0.05, and 0.10, respectively. All variables are defined in Table 2.

. Further, $\mathit{G}\mathit{O}\mathit{V}\mathit{P}$ exhibits a significant positive impact at the level of 1% (p-Value < 0.01) on $\mathit{T}\mathit{Q}$ in the case of the collective sample as well as in the case of UK firms and at the level of 5% (p-Value < 0.05) in the case of Turkish firms. This indicates that $\mathit{E}\mathit{S}\mathit{G},$ including $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$, $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$, and $\mathit{G}\mathit{O}\mathit{V}\mathit{P}$ have a greater significant association with $\mathit{T}\mathit{Q}$ than $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ as measurements of firms’ value. This implies that sustainability metrics are related to the firm’s overall market value and the long-term run market valuation rather than just the market value of stock or equity. This also signifies that sustainability issues matter for the wider interests of stakeholders rather than investors or equity holders. This is in line with Behl et al. [19], jin Kim et al. [60], and Y. Li et al. [13] who agree that ESG disclosure improves FV in the long run.

$\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$,$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$, $\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$, $\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$, $\mathit{B}\mathit{I}\mathit{N}\mathit{D}$, $\mathit{B}\mathit{E}\mathit{X}\mathit{P}$, and $\mathit{B}\mathit{D}\mathit{I}\mathit{V}$ also exhibit the same influence on $\mathit{T}\mathit{Q}$ compared to $\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$ and $\mathit{M}\mathit{T}\mathit{B}$ models except for some variations in the level of significance across these variables. However, $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ indicates a significant negative effect on $\mathit{T}\mathit{Q}$ at the level of 5% (p-Value < 0.05) in the case of Turkish firms. This indicates that the level of $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ in some Turkish firms does not associate positively with $\mathit{T}\mathit{Q}$ and firms with greater $\mathit{T}\mathit{Q}$ have low levels of $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$. This could be due to the fact that the shareholders in Turkish firms perceive that these expenses possibly do not add value to the firm; rather, $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$ are considered as a cost that affects the value of the firm. This is in agreement with Friske et al. [3], Fatemi et al. [59], Abdi et al. [18], and Ribot and Peluso (2003), who conclude that the cost of ESG initiatives has an impact on firms’ value.

Firms value is measured by TQ, following is the regression model:

$$\begin{gathered} \mathit{F}\mathit{i}\mathit{r}\mathit{m}\mathit{V}\mathit{a}\mathit{l}\mathit{u}\mathit{e}\mathbf{(}\mathit{T}\mathit{Q}{\mathbf{)}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{S}\mathit{G}{ }_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}}+ \\ {\mathit{\beta }}_{\mathbf{6} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{7} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}}+ \\ {\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}} \end{gathered}$$

Lagged Dependent Variable (Firms’ Value)

Table 8. Lagged independent variables—Collective Model.

	$\mathit{DV}=\mathit{P}\mathit{R}\mathit{I}\mathit{C}{\mathit{E}}_{\mathit{i}\mathit{t} }$		$\mathit{DV}=\mathit{M}\mathit{T}{\mathit{B}}_{\mathit{i}\mathit{t} }$		$\mathit{DV}=\mathit{T}{\mathit{Q}}_{\mathit{i}\mathit{t} }$
$\mathit{C}$	−19.142 ***	−21.768 ***	577.185 ***	702.665 ***	1.122 ***	1.153 ***
	−7.648	−8.959	7.810	8.746	29.167	28.703
$\mathit{E}\mathit{N}\mathit{V}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.068 ***		3.444 ***		−0.014 **
	14.019		23.397		−2.080
$\mathit{S}\mathit{O}\mathit{C}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.033 ***		2.953 ***		0.001 ***
	2.848		19.438		2.589
$\mathit{G}\mathit{O}\mathit{V}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.070 ***		1.204 ***		0.001 ***
	11.639		5.465		6.175
$\mathit{E}\mathit{S}{\mathit{G}}_{\mathit{i}\mathit{t}-\mathbf{1}}$		0.177 ***		0.679 ***		0.001 ***
		13.442		2.834		7.331
$\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.026 ***	0.001 ***	0.579 ***	0.458 ***	0.008 ***	0.008 ***
	6.230	4.371	4.033	4.143	2.660	6.299
$\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	−0.078 ***	−0.808 ***	−17.358 ***	−3.166 ***	−0.005 ***	−0.015 ***
	−6.308	−2.685	−1.317	−0.290	−1.213	−6.761
$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.034 ***	0.028 ***	0.438 ***	0.224 ***	0.000 ***	0.005 ***
	14.192	9.509	13.907	4.028	−3.207	−4.868
$\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.022 ***	0.015 ***	0.298 **	0.279 *	0.000 ***	0.002 ***
	7.193	8.223	1.222	1.685	3.652	4.765
$\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	−0.022 ***	−0.015 ***	1.654 ***	1.733 ***	−0.000 ***	−0.005 ***
	−3.261	−3.094	17.144	8.817	−3.610	−7.089
$\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.013 ***	0.001 ***	0.087 ***	0.401 ***	0.001 ***	0.001 ***
	1.917	4.281	6.657	3.778	18.849	14.239
$\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.011 ***	0.022 ***	1.104 ***	1.219 ***	0.000 **	0.002 ***
	3.418	4.440	8.419	8.398	1.395	4.903
$\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.037 ***	0.030 ***	0.770 **	0.253 ***	0.006 **	0.006 **
	6.282	6.288	2.295	5.622	2.122	2.203
$\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	−0.206 **	−0.047	−23.016 ***	−16.833 ***	−0.017 ***	−0.019 ***
	−2.651	−0.470	−4.233	−3.261	−33.803	−21.845
$\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	1.266 ***	1.214 ***	−3.321	−16.185 ***	0.025 ***	0.025 ***
	20.266	22.768	−1.011	−4.955	19.759	25.371
$\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.037 ***	0.034 ***	1.252 ***	1.240 ***	0.000 ***	0.006 ***
	10.440	10.726	26.564	11.547	7.048	5.734
$\mathit{Y}\mathit{E}\mathit{A}\mathit{R} \mathit{C}\mathit{O}\mathit{N}\mathit{T}\mathit{R}\mathit{O}\mathit{L}\mathit{S}$	YES	YES	YES	YES	YES	YES
R-squared	0.69	0.68	0.34	0.34	0.20	0.21
Adjusted R-squared	0.69	0.68	0.33	0.33	0.19	0.21
Durbin–Watson stat	0.44	0.44	0.37	0.36	0.61	0.62
F-statistic	249.27	263.00	56.98	66.87	28.09	35.55
Prob(F-statistic)	0.00	0.00	0.00	0.00	0.00	0.00

Note: ***, **, and * indicate a significance level at 0.01, 0.05, and 0.10, respectively. All variables are defined in Table 2.

and

Table 9. Lagged independent variables—Country-wide Model.

	$\mathit{DV}=\mathit{P}\mathit{R}\mathit{I}\mathit{C}{\mathit{E}}_{\mathit{i}\mathit{t} }$				$\mathit{DV}=\mathit{M}\mathit{T}{\mathit{B}}_{\mathit{i}\mathit{t} }$				$\mathit{DV}=\mathit{T}{\mathit{Q}}_{\mathit{i}\mathit{t} }$
	ESG Pillars Model		Overall ESG Model		ESG Pillars Model		Overall ESG Model		ESG Pillars Model		Overall ESG Model
	UK	Turkey	UK	Turkey	UK	Turkey	UK	Turkey	UK	Turkey	UK	Turkey
$\mathit{C}$	−58.318 ***	9.081 ***	−53.003 ***	8.208 ***	981.004 ***	204.283 ***	889.144 ***	299.445 ***	1.003 ***	0.754 ***	1.002 ***	1.086 ***
	−22.389	7.869	−17.587	10.414	8.617	6.042	5.461	10.660	24.450	18.082	17.812	11.382
$\mathit{E}\mathit{N}\mathit{V}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.061 ***	0.012 *			0.613 ***	0.180 *			0.021 ***	−0.015 **
	10.717	1.832			4.334	2.403			3.521	−3.094
$\mathit{S}\mathit{O}\mathit{C}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.042 ***	0.027 ***			1.486 ***	0.135 ***			0.001 ***	0.001 ***
	5.145	9.582			1.330	1.897			5.561	4.738
$\mathit{G}\mathit{O}\mathit{V}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.021 *	0.020 ***			0.618 ***	0.093 ***			0.002 ***	0.011 ***
	1.791	8.171			3.326	2.644			16.454	5.578
$\mathit{E}\mathit{S}{\mathit{G}}_{\mathit{i}\mathit{t}-\mathbf{1}}$			0.144 ***	0.046 ***			0.588 *	0.569 ***			0.004	0.001
			10.445	32.213			11.625	17.186			1.392	1.775
$\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.083 ***	0.000	0.054 ***	−0.001	0.040 **	0.017	0.565 **	−0.051 **	0.001 ***	0.000 **	0.001 ***	−0.001 ***
	9.663	−0.097	6.405	−0.534	4.274	0.557	2.041	−2.272	20.374	1.129	6.561	−4.012
$\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.957 ***	−0.357 **	0.938 **	−0.318 **	25.849 *	−10.505 ***	44.079 ***	−5.918 *	0.013 **	0.015 *	0.001 ***	0.025 *
	2.748	−2.445	2.142	−2.267	1.726	−2.829	3.313	−1.832	2.505	0.616	0.330	1.514
$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.021 ***	0.010 ***	0.016 ***	0.011 ***	1.551 ***	0.096	1.769 ***	0.060	0.000 **	0.006	0.003 ***	0.002 *
	8.097	7.393	6.100	5.028	36.417	1.337	22.200	1.632	−0.005	4.299	2.529	1.725
$\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.004 ***	0.007 ***	0.019 ***	0.008 **	1.892 ***	0.165 **	2.331 ***	0.160 ***	0.001 ***	−0.003	0.001 ***	0.004
	1.231	4.271	6.543	2.428	9.412	2.398	13.059	3.140	8.930	−2.400	6.540	3.814
$\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	−0.015 **	−0.001	−0.027 ***	0.001	1.972 ***	−0.075 **	1.625 ***	−0.116 ***	0.003	−0.004 *	−0.004 ***	−0.002 ***
	−2.081	−0.521	−4.634	0.417	6.253	−1.907	6.231	−4.092	1.602	−5.830	−6.172	−3.047
$\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.017 ***	0.005 **	0.030 ***	0.007 ***	0.819 ***	0.004	0.832 ***	0.172 ***	0.000 **	0.002	0.002 ***	0.001 ***
	5.104	2.511	5.683	2.871	5.200	0.083	4.440	5.226	2.302	5.008	3.964	5.382
$\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.002 ***	0.012 ***	0.007 **	0.013 ***	2.094 ***	0.082 ***	2.252 ***	0.188 ***	0.000 ***	0.017	0.006 *	0.002 *
	5.477	8.006	1.763	6.368	15.107	4.678	13.573	7.179	1.632	11.251	5.144	1.788
$\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.022 ***	0.008 ***	0.024 ***	0.009 **	0.245 **	0.043 **	0.505	0.128 ***	0.002 **	0.004 **	0.002 **	0.006 ***
	19.940	2.794	8.366	2.512	5.209	1.975	0.660	3.264	1.338	1.174	1.336	4.487
$\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.732 ***	−0.714 ***	0.727 ***	−0.751 ***	−33.276 ***	34.035	−26.166 ***	−13.110 ***	−0.013 ***	−0.021 ***	−0.013 ***	−0.067 ***
	7.402	−8.820	10.013	−10.050	−4.995	1.558	−4.316	−6.665	−6.143	−6.296	−4.135	−30.384
$\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	2.087 ***	0.354 ***	2.019 ***	0.441 ***	−11.175 **	−8.628 ***	−13.489 **	0.103	0.022 ***	0.026 ***	0.022 ***	0.078 ***
	18.294	7.878	22.343	8.134	−2.080	−6.078	−2.310	0.096	26.266	11.792	15.969	18.454
$\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}-\mathbf{1}}$	0.044 ***	0.002	0.039 ***	0.002	0.539 ***	0.048	0.448 ***	0.077 ***	0.000 ***	0.002 ***	0.008 ***	0.008 ***
	15.833	1.049	16.635	1.092	8.037	1.223	5.974	3.933	4.864	0.605	6.891	5.452
$\mathit{Y}\mathit{E}\mathit{A}\mathit{R} \mathit{C}\mathit{O}\mathit{N}\mathit{T}\mathit{R}\mathit{O}\mathit{L}\mathit{S}$	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES
R-squared	0.45	0.44	0.44	0.42	0.19	0.11	0.19	0.18	0.24	0.18	0.19	0.45
Adjusted R-squared	0.45	0.42	0.43	0.40	0.18	0.08	0.18	0.15	0.23	0.15	0.18	0.43
Durbin–Watson stat	0.41	0.75	0.43	0.74	0.36	0.57	0.36	0.56	0.65	0.39	0.60	0.58
F-statistic	69.27	21.59	76.60	23.85	19.91	3.46	23.04	7.06	26.11	6.05	22.18	26.62
Prob(F-statistic)	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

Note: ***, **, and * indicate a significance level at 0.01, 0.05, and 0.10, respectively, all variables are defined in Table 2.

provide an estimation of the effect of ESG pillars, overall ESG, other sustainability indicators, board characteristics, and firms’ specifics based on the lagged independent and control variables for the collective sample and country-wide samples, respectively. This is to test the link between historical sustainability and ESG performance with firms’ value. The results demonstrate that $\mathit{E}\mathit{N}\mathit{V}\mathit{P}$, $\mathit{S}\mathit{O}\mathit{C}\mathit{P}$, $\mathit{G}\mathit{O}\mathit{V}\mathit{P},$ and $\mathit{E}\mathit{S}\mathit{G}$ exhibit greater statistically significant effects compared to prior results across the models. This indicates that historical information of $\mathit{E}\mathit{S}\mathit{G}$ associates positively and significantly with FV ($\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$, $\mathit{M}\mathit{T}\mathit{B}$, and $\mathit{T}\mathit{Q}$).

Table 8 presents the OLS regression model based on lagged independent variables. Firm value is measured by PRICE, MTB, and TQ; the following is the regression model: $\begin{array}{l}\mathit{F}\mathit{i}\mathit{r}\mathit{m}\mathit{V}\mathit{a}\mathit{l}\mathit{u}{\mathit{e}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{S}\mathit{G}{ }_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}-\mathbf{1}}+\\ {\mathit{\beta }}_{\mathbf{6} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{7} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}-\mathbf{1}}+\\ {\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}} \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{3}\right)\end{array}$ $\begin{array}{l}\mathit{P}\mathit{R}\mathit{I}\mathit{C}{\mathit{E}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{S}\mathit{G}{ }_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}-\mathbf{1}}+\\ {\mathit{\beta }}_{\mathbf{6} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{7} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}-\mathbf{1}}+\\ {\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}} \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{3}\mathbf{a}\right)\end{array}$ $\begin{array}{l}\mathit{M}\mathit{T}{\mathit{B}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{S}\mathit{G}{ }_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}-\mathbf{1}}+\\ {\mathit{\beta }}_{\mathbf{6} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{7} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{12} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}-\mathbf{1}}+\\ {\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}} \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{3}\mathbf{b}\right)\end{array}$

$$\begin{gathered} \mathit{T}{\mathit{Q}}_{\mathit{i}\mathit{t}}=\mathit{\alpha }+{\mathit{\beta }}_{\mathbf{1} }\mathit{E}\mathit{S}\mathit{G}{ }_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{2} }\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{3} }\mathit{E}\mathit{N}\mathit{V}\mathit{C}\mathit{O}{\mathit{N}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{4} }\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}{\mathit{C}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{5} }\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}{\mathit{M}}_{\mathit{i}\mathit{t}-\mathbf{1}}+ \\ {\mathit{\beta }}_{\mathbf{6} }\mathit{B}\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{7} }\mathit{B}\mathit{I}\mathit{N}{\mathit{D}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{8} }\mathit{B}\mathit{E}\mathit{X}{\mathit{P}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{9} }\mathit{B}\mathit{D}\mathit{I}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{10} }\mathit{S}\mathit{I}\mathit{Z}{\mathit{E}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\mathit{\beta }}_{\mathbf{11} }\mathit{L}\mathit{E}{\mathit{V}}_{\mathit{i}\mathit{t}-\mathbf{1}}+ \\ {\mathit{\beta }}_{\mathbf{12} }\mathit{R}\mathit{E}\mathit{V}\mathit{G}{\mathit{R}}_{\mathit{i}\mathit{t}-\mathbf{1}}+{\displaystyle \sum }{\mathit{\beta }}_{\mathit{m} }\mathit{YearDummy}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}} \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\left(\mathbf{Model}\mathbf{3}\mathbf{c}\right) \end{gathered}$$

The results also reveal that $\mathit{E}\mathit{N}\mathit{V}\mathit{E}\mathit{X}\mathit{P}$, $\mathit{E}\mathit{S}\mathit{G}\mathit{C}\mathit{O}\mathit{N}$,$\mathit{S}\mathit{U}\mathit{S}\mathit{I}\mathit{N}\mathit{C}$, $\mathit{E}\mathit{N}\mathit{V}\mathit{T}\mathit{E}\mathit{A}\mathit{M}$, $\mathit{B}\mathit{S}\mathit{I}\mathit{Z}\mathit{E}$, $\mathit{B}\mathit{I}\mathit{N}\mathit{D}$, $\mathit{B}\mathit{E}\mathit{X}\mathit{P}$, and $\mathit{B}\mathit{D}\mathit{I}\mathit{V}$ exhibit better statistical power than the earlier estimated results. This indicates that historical information on sustainability and board indicators have a greater significant association with FV ($\mathit{P}\mathit{R}\mathit{I}\mathit{C}\mathit{E}$, $\mathit{M}\mathit{T}\mathit{B}$, and $\mathit{T}\mathit{Q}$) than revealed in the earlier models. This signifies that the past ESG and sustainability performance influenced FV over the long run.

Discussion and Implications

The current study’s findings are consistent with Kim et al. [60], who conclude that environmental, social responsibility, and CG are three subcategories of ESG that have varying effects on business value. Fatemi et al. (2018) consistently show that ESG score increases business value. Z. Li et al. [51] find that the market reacts more positively to ESG initiatives than sustainability initiatives. Similarly, Z. Li et al. [51] indicate that superior ESG disclosure increases FV by increasing firm stakeholder trust. Likewise, W. C. Wong et al. [57] advocate that a firm benefits stakeholders by pursuing an ESG agenda. Consistently, Qureshi et al. [23] provided evidence of a positive impact of sustainability disclosure on FV, implying that superior management practices increased stakeholder trust. According to Abdi et al. [18], a firm’s involvement in social and environmental activities is positively and considerably rewarded by greater financial efficiency. Accordingly, ESG plays an important moderating function in boosting the influence of enterprise risk management on FV [16]. In contrast, Zehir and Aybars [56] report that socially responsible investment has no association with portfolio performance. This makes it difficult to deliver competitive outcomes while integrating ESG indicators to positively influence business value and ensure a decent public reputation [33].

Our findings are also consistent with previous studies [13,33,46,60]. Y. Li et al. [13] suggest that firms that disclose more about their ESG may be more attractive to financial investors and other significant stakeholders. Thus, ESG disclosure enhances FV in the long run. That is because ESG is a business strategy that allows companies to thrive and sustain their operations over time by pursuing cooperation, consensus, and symbiotic relationships with many stakeholders. This is based on the view that firm sustainability management is widely accepted in business circles as a realistic long-term strategy for corporate survival [60]. In the same respect, Aksoy et al. [33] indicate that sustainability is acknowledged as a long-term aim that forms socially and ecologically conscious companies to produce competitive outcomes while embracing ESG measures to influence FV and assure a decent public reputation favorably. Likewise, Chouaibi and Chouaibi [46] indicate that ESG disclosure can be utilized as an effective communication tool to demonstrate the company’s ability to establish and retain a competitive advantage, attracting more long-term investors. Similarly, Alsayegh et al. [80] suggest that firms must consider all stakeholders to build long-term corporate sustainability value. For a firm to be sustainable, it must be financially secure, produce long-term value, and have a low environmental impact. Accordingly, ESG activities will enhance a company’s long-term value by meeting social commitments, environmental duties, and strengthening its brand [99].

5. Conclusions

The present study investigates the effect of sustainability issues measured by ESG on FV in the UK and Turkey. The study is based on secondary data extracted from the Refinitiv Eikon database for 319 firms. The sample comprises 238 firms from UK and 81 firms from Turkey for the period from 2016 to 2021. FV has been measured using several metrics, including stock price, market-to-book value, and Tobin’s Q. Similarly, ESG indicators comprise the environmental pillar, social pillar, governance pillar, and the overall ESG indicator. Further, the level of environmental expenses, ESG controversies, sustainability incentives, and environmental teams are also utilized as ESG variables. Similarly, board characteristics include board size, independence, expertise, and diversity. Firms’ specifics include leverage, size, and revenue growth. Panel data analysis using a fixed effect model is used to estimate the results.

The results reveal that ESG indicators, including the environmental, social, governance, and overall ESG, are associated significantly with firms’ value. This suggests that ESG, which includes ESG pillars, has a stronger significant association with Tobin’s Q than stock prices and market-to-book value as measures of firms’ value. Sustainability indicators are linked to the firm’s total market value rather than just the market value of its stock or equity. This implies that sustainability performance is related to the firm’s overall market value and the long-term run market valuation rather than just the market value of the stock. This also indicates that sustainability issues matter for the wider interests of stakeholders rather than investors or equity holders in the UK and Turkey. The results also reveal that ESG controversies raised by media are associated positively and significantly with FV in the case of UK firms; however, it is observed to have a negative influence in the case of Turkish firms. Further, the results report that the existence of an environmental team links positively and significantly with FV in both Turkish and UK firms. However, sustainability incentives exhibit a statistically insignificant association with FV in both cases of UK and Turkish firms. With regard to board characteristics, while board independence, board expertise, and board diversity exhibit a significant and positive association with firms’ value, board size negatively affects firms’ value. Finally, the results find that leverage has a negative relationship with FV in the case of Turkish but exhibits a positive relationship in the case of UK firms. However, both larger firm size and higher revenue growth are associated with greater firms’ value.

The current study bridges an existing gap in the existing stock of knowledge; it contributes to the strand literature in several ways. Using a comparative sample from UK and Turkey, considered developed and ideal countries in their sustainability practices, we aim to explore the relationship between ESG issues and firms’ value. We assess the influence of sustainability issues on firms’ values in different contexts, using several ESG metrics and some other sustainability indicators. The present study provides empirical implications for regulatory authorities to provide a comprehensive framework or guidelines for ESG reporting. Importantly, we provide evidence based on different institutional, regulatory frameworks, and legislation. Accordingly, the current study provides a unique contribution by introducing empirical evidence from Europe based on a sample of two leading economies.

This study has some limitations that shed light on possible future research directions. First, the study is built on a comprehensive framework based on ESG indicators rather than investigating a specific issue of environmental, social, or governance sustainability measures. Future studies may explore some specific issues such as environmental dimensions, emissions, innovations, or any other specific issue. Second, the current study is focused on a sample from two countries; future studies may include some other countries, especially the US and Canada. Finally, future studies may extend the effort made by the current study to investigate sector and industry-wide differences.