ESG Performance and Tourism Enterprise Value: Impact Effects and Mechanism Analysis

Abstract

In the context of global sustainable development, ESG has assumed a pivotal role in evaluating corporate performance. To identify the causal effect of ESG disclosure on firm value, we implement a difference-in-differences (DID) analysis using panel data from A-share listed tourism companies between 2012 and 2020. The study revealed that ESG disclosure has significantly increased tourism corporate value by alleviating financing constraints, reducing financial risks, and attracting green investors. The validity of our conclusion is affirmed through a series of robustness checks, including the parallel trend test, placebo test, bacon decomposition, propensity score matching (PSM), and system generalized method of moments (GMM). Heterogeneity analysis indicates that the positive impact of ESG disclosure on the value of tourism firms is more pronounced in samples with state-owned property nature, a separation of CEO and chairman roles, and low green total factor productivity. Furthermore, this effect is significantly stronger for firms in the accommodation and catering and tourism sightseeing sectors. This study contributes by empirically validating the internal transmission channels through which ESG performance affects firm value in the tourism sector, while also demonstrating the heterogeneous nature of this relationship, thereby providing nuanced evidence for developing differentiated ESG strategies.

1. Introduction

Tourism is a highly integrated industry that naturally has a modeling role in leading social trends (Li Hanliang et al., 2023) [1]. According to the United Nations Environment Programme (UNEP), the contribution of tourism to global climate change ranges from 5% to 14%, and its carbon emissions are growing at an average annual rate of 2.5%. The trajectory of the tourism industry is crucial to the realization of China’s “dual-carbon” goal. Formally, this goal commits the nation to hitting peak CO₂ emissions before 2030 and attaining carbon neutrality before 2060. Therefore, it is of great significance to guide the tourism industry in the direction that is conducive to sustainable development. As the year 2025 marks the end of China’s 14th Five-Year Plan and the beginning of the 15th Five-Year Plan, there is an urgent need for China to comprehensively assess the effectiveness of high-quality economic development and forge a path for the new phase at this critical juncture. Just as Huang Sujian et al. (2018) [2] pointed out, high-quality economic development is ultimately realized through high-quality enterprise development. Enterprises, as the most dynamic market participants, form the micro foundation for high-quality economic development. Guiding enterprises to transition toward low-carbon practices while actively fulfilling social responsibilities is an inevitable requirement for advancing sustainable development.

The ESG framework was formally introduced by the United Nations in 2004. It is a comprehensive corporate evaluation index with sustainable development as its core and integrating environment, society, and governance. As a comprehensive evaluation system for assessing the green development, social value and governance effectiveness of enterprises, ESG embodies the development concept of enterprises in pursuit of the unity of social and economic values, which is highly compatible with China’s “dual-carbon” goal and is consistent with the principles of sustainable development. Today, ESG has become a key indicator for measuring the sustainability level of enterprises (Zhou Zejiang et al., 2023) [3]. To date, Chinese government ministries and regulators have issued more than 50 ESG-related sustainability guidelines. ESG in China has shifted from voluntary CSR to a strategic imperative driven by government policy and competitive pressures.

ESG factors influence many aspects of a company’s operational activities. In the area of risk management, for instance, some scholars argue that ESG practices can directly reduce a company’s unsystematic risks (Lian Yonghui et al., 2023) [4]. Regarding the cost of capital, empirical evidence indicates that ESG disclosure reduces the cost of equity by lowering investor return requirements(Mathath al., 2025) [5]. In terms of corporate resilience, Wang et al. (2023) [6] argue that strong ESG performance enhances financial resilience by improving investment efficiency and operational efficiency. In terms of corporate value, Tang et al. (2024) [7] believe that positive ESG performance builds investor confidence, driving corporate value. However, there are also some opposing views. Early scholars argued that ESG practices require a significant investment of resources, which may crowd out resources from other productive investments, thus negatively impacting a firm’s financial performance (Schuler et al., 2006) [8]. Garsaa (2025) [9] further noted out that when firms face greater external pressures, passive ESG practices based on legitimacy pressures may interfere with routine corporate decision-making, which is not conducive to the enhancement of firm value. In addition, it is also argued that ESG performance and firm value are not linearly related, but rather different characteristics in different contexts. Nolle (2016) [10] found a U-shaped link between ESG and financial performance: before reaching a critical threshold, ESG and financial performance are negatively correlated, while beyond that threshold, it becomes positively correlated. Additionally, Garcia et al. (2020) [11] found that there is a positive correlation for companies in developed countries, while for companies in emerging markets, this relationship is negative.

Despite growing scholarly interest in ESG’s corporate impact, findings from general studies may not apply to specific industries, leaving a significant research gap in understanding ESG’s effects on the tourism sector. The same is true at the practical level, as the disclosure rate of China’s social service industry (including hotels and scenic spots) is less than 50% in 2024, and only 41.6% of hotel companies issue independent reports, so research on tourism industry is necessary. Policy guidance and external supervision are important, but stimulating the endogenous power of enterprises is the key (Wang Linlin et al., 2022) [12]. The main objective of business management is to maximize enterprise value, while sustainable development often means that enterprises need to pay extra costs for environmental and social benefits. Given these unique challenges and the current lack of industry-specific research, understanding how ESG practices can create value for tourism enterprises becomes not just an academic question but a practical imperative. Therefore, based on the perspective of enterprise value, this study aims to explore the impact of ESG disclosure on the market value of tourism firms, providing practical guidance for the sustainable development of the tourism industry.

This paper makes significant contributions across theoretical, practical, and methodological dimensions. Theoretically, it contributes to the theoretical literature by being one of the first to integrate ESG performance and firm value within a unified analytical framework specifically for the tourism sector. Practically, our empirical analysis provides robust evidence on the correlation between ESG performance and firm value, offering practical recommendations for tourism companies to strategically enhance market valuation through sustainable practices. Methodologically, by employing multi-period DID approach, the study effectively mitigates endogeneity concerns.

2. Literature Review and Research Hypotheses

2.1. ESG Disclosure and Market Value

Challenging the traditional shareholder primacy model, stakeholder theory holds that a firm’s market value relies on meeting stakeholder demands (Talan, 2024) [13]. Tourism enterprises consciously fulfilling their ESG responsibilities can win the favor of stakeholders and improve their ability to utilize resources and gain competitive advantages (Houston et al., 2022) [14]. Moreover, moral capital mitigates external risks for firms committed to environmental and social responsibility (Olofsson et al., 2021) [15]. This means that ESG investment by tourism companies will create a relatively stable environment for their development.

Agency problems stem from conflicts of interest between principals and agents (Healy et al., 2001) [16]. ESG performance, particularly its governance dimension, can signal effective mitigation of these problems. Li Jinglin et al. (2021) [17] point out that good ESG performance signals strong governance, which lowers agency costs. Concurrently, ESG practices reduce single-agent control risks through stakeholder governance networks, thereby enhancing corporate value (Sharma et al., 2025) [18]. In the tourism industry, its “asset-light operation” and “high service orientation” will enhance the role of ESG in curbing agency costs, thus affecting firm value.

Information asymmetry in economic activities can be mitigated by credible signals. Based on signal theory, strong ESG performance serves as such a signal, enhancing corporate image and investor confidence (Shaikh et al., 2022; Xi Longsheng et al., 2022) [19,20]. In addition, ESG disclosure enhances corporate transparency and strengthens stakeholder engagement, which leads to better financial performance (Agapova et al., 2025) [21]. This signaling effect is particularly potent in the tourism industry, which is characterized by high consumer exposure and resource dependence. For instance, ESG disclosures like “green hotel certification” or “eco-attractions management” directly signal sustainability to consumers, thereby influencing market competitiveness (Millar, 2009) [22]. However, a significant challenge arises when disclosures diverge from actual practices, a phenomenon known as “greenwashing”. According to ICMA (2023) [23], greenwashing occurs when sustainability claims are not substantiated by genuine practices. Studies have documented instances where firm’s public commitments to climate action or social responsibility are not substantiated by their operational investments or environmental outcomes (Marquis, Toffel, & Zhou, 2016; Dyck, Lins, Roth, & Wagner, 2019) [24,25]. This misrepresentation erodes investor confidence and undermines sustainable finance market credibility. Consequently, authentic and proactive communication becomes a valuable signal to the market, as evidence suggests that investors place a premium on voluntary disclosures that genuinely enhance firm transparency (Flammer, Toffel, & Viswanathan, 2021) [26].

Building on the above analyses, the research hypothesis H1 is proposed:

H1. 

ESG disclosure has a positive impact on the value of tourism companies.

2.2. Mediating Factors

We believe that ESG disclosure mainly promotes the value enhancement of tourism enterprises through the following three paths.

Firstly, alleviating financing constraints. Capital is a crucial foundation for companies to achieve sustainable development, and a stable source of funds is vital for enhancing corporate value. However, tourism investment projects are usually accompanied by low returns, large capital requirements and long payback cycles, so tourism enterprises are often subject to tremendous investment pressure. ESG ratings provide investors with comprehensive information, thereby mitigating investment risks and reducing the required rate of return, which ultimately leads to lower financing costs for firms (Xu et al., 2025) [27]. In tourism, this effect may be further amplified by the industry’s dependence on natural resources.

Secondly, reducing financial risks. Increasing business complexity heightens corporate uncertainty. Unfortunately, the tourism industry happens to be highly dependent on the external environment. On the one hand, the seasonality of tourism creates unstable revenue for tourism enterprises; on the other hand, the tourism industry is extremely vulnerable to force majeure events like natural disasters and political unrest. A case in point is the COVID-19 pandemic of 2020, which caused more than US$1 trillion in losses to the global tourism industry. Proactive fulfillment of ESG responsibilities enables firms to accumulate ethical capital, thereby enhancing resilience against external uncertainties (Yang et al., 2025) [28].

Lastly, increasing green investment levels. Green investors are a group of socially responsible investment entities whose investment decisions are aimed at screening out projects with social and environmental benefits, and prompting enterprises to deeply integrate green concepts into their daily business activities (Jiang et al., 2021) [29]. ESG disclosure helps to shape a good image of responsible and environmentally conscious enterprises and win the favor of green investors, who, through their participation in the corporate growth and strategy, not only improve the level of green investment and environmental performance, but also enhance the sustainable development capability of enterprises (Tang et al., 2025) [30].

The preceding discussion leads to the following hypotheses (Figure 1):

H2a. 

ESG disclosure enhances the value of tourism companies by alleviating financing constraints.

H2b. 

ESG disclosure enhances the value of tourism companies by reducing financial risks.

H2c. 

ESG disclosure enhances the value of tourism companies by attracting green investors.

3. Research Design

3.1. Model Specification

3.1.1. Baseline Model

Our research evaluates the treatment effect of ESG disclosure by treating it as a quasi-natural experiment. This identification strategy, which views the disclosure as the “event,” is conceptually aligned with the event study methodology. The event study methodology is a standard approach for assessing the impact of a specific event by analyzing changes over time, typically by comparing pre- and post-event outcomes(Bergmann et al., 2015) [31]. Therefore, we construct a baseline model using a two-way fixed effects staggered DID technique, as recommended by Beck et al. (2010) [32]. This methodological choice enables us to compare the market value changes in tourism firms that initiated ESG disclosure (the treatment group) with those that did not (the control group), before and after the disclosure event, thereby offering a robust estimation of the policy’s net effect.

$$Tobin’s Q_{it}={\lambda }_0+{\lambda }_1{discm}_{it}+\gamma {Controls}_{it}+{Firm}_i+{Year}_t+{\epsilon }_{it}$$

(1)

where the explained variable $Tobin’s Q_{it}$ is Tobin’s Q for firm i in year t; ${discm}_{it}$ is the core explanatory variable, if MSCI has published rating data for company i in year t, then ${discm}_{it}$ = 1, otherwise ${discm}_{it}$ = 0; ${Controls}_{it}$ is a series of control variables; ${Firm}_i$ is individual fixed effects; ${Year}_t$ is time fixed effects; ${\lambda }_0$ is the intercept term of the model; ${\lambda }_1$ is the coefficient of interest, which captures the treatment effect; $\gamma$ is the vector of coefficients for the control variables; ${\epsilon }_{it}$ is a random error term.

3.1.2. Difference in Differences Model

A key prerequisite for the difference-in-differences method is the parallel trends assumption, which requires that the treatment and control groups share the same trend in the outcome prior to treatment. We refer to the measurement equation by Huang Wei et al. (2022) [33] and takes the ESG disclosure time point as the relative time reference system for parallel trend testing. It should be noted that since MSCI began rating Chinese A-share listed companies in 2018, there are few observations in the three years before and after ESG disclosure. Therefore, referring to the practice of Pei Li et al. (2016) [34], this part of the observation value is merged for testing, and the year before ESG disclosure is taken as the base period. The finally constructed dynamic double difference model is as follows:

$$\begin{array}{cc}\hfill Tobin’s Q_{it}=& \alpha +{\beta }_s^{precut}\left[D_i\times I\left(t-T_D<-3\right)\right]+{\sum }_{-3}^{-2}{\beta }_s^{pre}\left[D_i\times I\left(t-T_D=s\right)\right]\hfill \\ \hfill & +{\sum }_0^2{\beta }_s^{post}\left[D_i\times I\left(t-T_D=s\right)\right]+{\beta }_s^{postcut}\left[D_i\times I\left(t-T_D>2\right)\right]\hfill \\ \hfill & +\gamma {Controls}_{it}+{Firm}_i+{Year}_t+{\epsilon }_{it}\hfill \end{array}$$

(2)

where $D_i$ is a dummy variable that equals 1 if firm i is treated, and 0 otherwise; $\mathrm{I}(·)$ is an indicator function, $T_D$ is the current period of ESG disclosure, with the relative time to ESG disclosure as the reference system $(t-T_D=s)$, where the pre-period of ESG disclosure $(s=-1)$ is the baseline period, and the other variables are consistent with the baseline model. The change in coefficients ${\beta }_s$ in the model reflects the dynamic change in the impact of ESG ratings on corporate value. If ${\beta }_s^{precut}$ and ${\beta }_s^{pre}$ are not significant, while ${\beta }_s^{post}$ and ${\beta }_s^{postcut}$ are significant, it proves that the model passes the parallel trend test.

3.2. Variable Setting

3.2.1. Core Explanatory Variable

At present, ESG rating agencies have not yet formed a unified measurement standard, and the consistency of different evaluation systems is low. The ESG ratings from different agencies for the same firm often show significant discrepancies (Hu et al., 2024) [35]. In view of this, we use the first published ESG ratings of companies by rating agencies as the shock variable of the study. Specifically, if MSCI has published the rating data of company i in year t, it belongs to the treatment group, assigned a value of 1; otherwise, it belongs to the control group, assigned a value of 0.

3.2.2. Explained Variable

Tobin’s Q measures a firm’s market value relative to the replacement cost of its assets, which is widely used in management and financial research to assess the market response of a firm. It reflects both the market value of a firm and its growth opportunities, providing stakeholders with predictive information about the future development potential of enterprises. Therefore, we adopt the indicator of firm’s Tobin’s Q (Tobin’s Q) to measure the market value of the firm.

3.2.3. Control Variables

Consistent with prior research, this article controls important variables that affect the value of enterprises, including: enterprise listing age (Listage), enterprise scale (Size), enterprise debt ratio (Lev), total asset turnover rate (ATO), cash flow return on assets (Cashflow), board size (Boardsize), management fee rate (Mfee), the shareholding ratio of the largest shareholder (Top1) and equity balance (Balance).

Table 1. Variables Definition and Descriptive Statistics.

Variable	Definition	Obs	Mean	Std.Dev.	Min	Max
Tobin’s Q	Market value/ Asset replacement cost	408	2.002	1.317	0.825	13.53
discm	1 if MSCI publishes the company’s ESG rating for the year, 0 otherwise	408	0.145	0.352	0	1
Listage	(Current year − listing year) + 1	408	14.96	6.902	1	29
Size	Natural logarithm of total assets	408	22.54	1.680	19.88	27.97
Lev	Total liabilities/Total assets	408	0.421	0.219	0.0503	0.895
ATO	Operating revenue/ Average total assets	408	0.520	0.400	0.0162	2.796
Cashflow	Net cash flow from operating activities/Total assets	408	0.0616	0.0742	−0.310	0.292
Boardsize	Natural logarithm of total board members	408	2.187	0.193	1.386	2.708
Mfee	Management expenses/Operating revenue	408	0.135	0.161	0.0123	2.187
Top1	Largest shareholder’s shareholding/Total shares	408	0.393	0.148	0.122	0.713
Balance	Sum of shareholdings of the second to fifth largest shareholders/Largest shareholder’s shareholding	408	0.622	0.507	0.0267	2.577

presents the definitions and descriptive statistics of the variables.

3.3. Data Sources

Referring to National Economic Industry Classification and China’s National Statistical Classification of Tourism and Related Industries (2018) [36,37], we select tourism enterprises in China’s A-share listed companies from 2012 to 2020 as the initial research sample. The sample data are processed as follows: (1) Removing samples where the main business is not tourism-related; (2) excluding ST and *ST company samples; and (3) eliminating samples with severe data missing or containing outliers. A total of 408 unbalanced panel sample observations are obtained. All continuous variables are winsorized at the 1st and 99th percentiles. Corporate financial data are sourced from the CSMAR database, and ESG rating data are obtained from the MSCI official website (www.msci.com) and the Bloomberg database.

4. Empirical Results Analysis

4.1. Baseline Regression

Table 2. Benchmark Regression.

	(1)	(2)
Variable	Tobin’s Q	Tobin’s Q
discm	1.0560 **	1.2372 ***
	(0.4048)	(0.3826)
Listage		0.0527 *
		(0.0292)
Size		−0.4287 **
		(0.1952)
Lev		0.5275
		(0.6614)
ATO		0.2074
		(0.3144)
Cashflow		3.2899 **
		(1.3197)
Boardsize		−0.0324
		(0.4923)
Mfee		0.8569 ***
		(0.3103)
Top1		−2.1045
		(1.3783)
Balance		−0.2298
		(0.4128)
_cons	1.7160 ***	10.8417 **
	(0.1237)	(4.2667)
Controls	No	Yes
Firm Effect	Yes	Yes
Year Effect	Yes	Yes
Obs.	408	408
Adj.R-squared	0.2342	0.3009

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The numbers in parentheses are firm-level clustered robust standard errors, the same applies below.

reports the results of benchmark regressions on the impact of ESG disclosure on tourism firm value based on Equation (1). All regression analyses control for firm and year fixed effects and use firm-level clustering robust standard errors. Column (1) shows a significantly positive regression coefficient for ESG disclosure without adding control variables. Column (2) shows that the coefficient on ESG disclosure remains significantly positive at the 1% level after controlling for a range of additional variables. Thus, the research hypothesis 1 is proved, i.e., ESG disclosure has a significant positive promotional effect on the market value of tourism companies. This result suggests that for tourism firms, enhancing ESG disclosure is a strategic initiative that can effectively increase their market value.

4.2. Robustness Tests

Potential endogeneity issues may interfere with the benchmark regression results. Accordingly, we conduct the following robustness checks to verify the consistency of our empirical results:

4.2.1. Dynamic Test of the Relationship Between ESG Disclosure and Market Value

After initially examining the contribution of ESG disclosure to firm value, we further track the dynamics of the relationship based on model (2). If the dynamic effects of the results support the parallel trend hypothesis, it indicates that the estimates using double difference model are reliable. The results in Figure 2 reveal a lack of statistically significant pre-treatment differences in Tobin’s Q between treatment group and control group, which validates the key parallel trend assumption required for the DID model. After the disclosure point, the coefficient of Tobin’s Q value rises significantly and increases year by year thereafter, indicating that ESG disclosure has a significant contribution to the value of tourism enterprises.

4.2.2. Placebo Test

Following the approach of La Ferrara et al. (2012) [38], we construct a counterfactual scenario by replacing the individuals in the treatment group with placebo test. Should the results remain significant across placebo test, it would indicate that the baseline regression is likely affected by other random factors, undermining its validity. This process is repeated 1000 times, the test results are shown in Figure 3. According to the analysis, the distribution of the regression coefficients for the pseudo-treatment variable is approximately normal and centered around zero. This central tendency is visually represented by the dashed vertical line at zero in the figure. Crucially, this distribution is far to the left of the solid red line, which marks the actual treatment effect (1.24) from our main analysis, indicating that the placebo effects are substantially smaller. Additionally, most p-values are above 0.10, meaning that the coefficients of most regressions are not significant at the 10% level. Thus, it can be concluded that the effect of ESG ratings on the value of tourism enterprises is not caused by other random factors.

4.2.3. Goodman-Bacon Decomposition

Goodman-Bacon (2021) [39] points out that all 2 × 2 DIDs can be roughly divided into three categories: ① treated vs. never treated, ② early treated vs. late treated, and ③ late treated vs. early treated. The last category is referred to as the “prohibited control group,” as it can lead to severe bias in the TWFE estimator and even yield opposite results. Therefore, we use Bacon decomposition to test the rationality of the assignment of treatment and control groups in the model. The decomposition results are shown in

Table 3. Bacon Decomposition.

DID Comparison	Avg DID Est	Weight
Early Treated vs. Late Treated	0.092	0.046
Late Treated vs. Early Treated	−0.622	0.022
Treated vs. Never Treated	1.199	0.877
Treated vs. Always Treated	0.410	0.056

and Figure 4. As shown in Figure 4, the red dashed line represents the overall weighted average treatment effect (1.064), a value that is overwhelmingly driven by the most reliable “treated vs. never treated” comparison, which carries a high weight of 87.7%. The second group (late treated vs. early treated), which may lead to biased regression results, having the lowest weight of 2.2%. From the Bacon decomposition results above, it can be concluded that the results obtained from the TWFE staggered DID are robust.

4.2.4. Propensity Score Matching

To mitigate endogeneity from selection bias, we combine Propensity Score Matching (PSM) with DID approach. PSM first creates a statistically comparable control group by matching non-disclosing firms to disclosing firms based on pre-event characteristics. The DID model on this matched sample then captures the differential change between groups, yielding a robust causal estimate. It should be noted that Lee et al. (2022) [40] pointed out that the more covariates of propensity score matching, the more significant the multi-dimensional differences between individuals, and the fewer sample pairs meet the similarity conditions in the matching process. To avoid this, we select the debt-to-asset ratio (Lev), cash flow ratio (Cashflow), board size (Boardsize), and equity balance degree (Balance) as covariates, and uses 1:1 nearest neighbor matching to match the control group for the treatment group. Referring to the approach of Li Miao et al. (2024) [41], we also adopt 1-to-3 nearest neighbor matching and kernel matching to make full use of the existing data. As shown in

Table 4. Propensity Score Matching.

Variable	(1)	(2)	(3)
	Tobin’s Q	Tobin’s Q	Tobin’s Q
	1:1 Nearest Neighbor Matching	1:3 Nearest Neighbor Matching	Kernel Density Matching
discm	0.7013 ***	0.7131 ***	0.9041 ***
	(0.2480)	(0.2443)	(0.2495)
_cons	1.6426	1.4970	1.8609 *
	(1.6064)	(0.9571)	(0.9376)
Controls	Yes	Yes	Yes
Firm Effect	Yes	Yes	Yes
Year Effect	Yes	Yes	Yes
Obs.	161	270	394
Adj.R-squared	0.3069	0.2878	0.3146

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The numbers in parentheses are firm-level clustered robust standard errors.

, the positive impact of ESG disclosure on firm value remains significant after applying PSM-DID, further verifying the robustness of the previous conclusions.

4.2.5. System Generalized Method of Moments

Corporate financial performance exhibits continuity, meaning the current value depends on its lag. Therefore, by adding the first-order lag term of Tobin’s Q value to the benchmark regression model, we construct a dynamic panel model for estimation. The specific model is set as follows:

$$\begin{array}{rr}Tobin’s Q_{it}=& {\theta }_0+{\theta }_{1}LTobin’s Q_{it-1}+{\theta }_2{discm}_{it}\hfill \\ \hfill & +\gamma {Contorls}_{it}+{Firm}_i+{Year}_t+{\epsilon }_{it}\hfill \end{array}$$

(3)

where $LTobin’s Q_{it-1}$ is the first-order lag term of the firm’s Tobin’s Q, and other symbols are the same as in Equation (1).

We use the System Generalized Method of Moments (GMM) proposed by Blundell and Bond (1998) [42] to address dynamic panel bias. The AR(1) and AR(2) tests yield p-values of 0.039 and 0.127, respectively, indicating the presence of first-order serial correlation but no second-order serial correlation; Hansen test value is 0.395, indicating acceptance of the null hypothesis, and the instrumental variables are jointly valid.

Table 5. System GMM Estimation.

Variable	(4)	(5)
	Tobin’s Q	Tobin’s Q
	Two-Way Fixed Effects Model (TWFE)	Generalized Method of Moments Model (GMM)
discm	0.9588 ***	1.1850 ***
	(0.2887)	(0.3969)
L.Tobin’s Q	0.4522 ***	0.7041 ***
	(0.1482)	(0.1456)
_cons	14.4669 **	-
	(6.9970)	-
Controls	Yes	Yes
Firm Effect	Yes	Yes
Year Effect	Yes	Yes
Obs.	353	353
Adj.R-squared	0.4264	-

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The numbers in parentheses are firm-level clustered robust standard errors.

reports the TWFE estimates in column (4) and the System GMM estimates in column (5). The significant first-order lagged dependent variable coefficient confirms tourism enterprise value persistence, justifying dynamic panel analysis. Whether using static or dynamic panel estimates, ESG disclosure has a significant positive contribution to tourism firm value.

4.2.6. Replacing the Core Explanatory Variable and Explained Variable

We conduct regression analyses using the shock variable discs generated from the ESG ratings of SynTao Green Finance, which takes 1 if SynTao Green Finance publishes data on firm i’s ratings in year t, and 0 otherwise. In addition, we introduce the Price-to-Book Ratio (PBratio) and Price-to-Tangible Book Value Ratio (PTratio) as proxies for the explanatory variables. The regression results are shown in

Table 6. Replacement of Explanatory and Explained Variables.

Variable	(1)	(2)	(3)
	Tobin’s Q	PBratio	PTratio
discs	0.4223 **
	(0.1767)
discm		2.0300 ***	1.2903 ***
		(0.6706)	(0.4105)
_cons	7.8354	28.2994 ***	8.6584 **
	(4.9950)	(6.5528)	(4.2226)
Controls	Yes	Yes	Yes
Firm Effect	Yes	Yes	Yes
Year Effect	Yes	Yes	Yes
Obs.	408	408	393
Adj.R-squared	0.2392	0.3788	0.2534

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The numbers in parentheses are firm-level clustered robust standard errors.

, and the baseline conclusions remain valid whether the explanatory or interpreted variables are replaced.

5. Mechanism Test

After verifying the benchmark effect, we set up a mechanism test model based on model (1) to reveal the transmission mechanism of ESG information disclosure affecting corporate value creation, and conducted supplementary analysis based on Sobel and Bootstrap test.

$$M_{it}={\mu }_0+{\mu }_1{discm}_{it}+\gamma {Contorls}_{it}+{Firm}_i+{Year}_t+{\epsilon }_{it}$$

(4)

$$\begin{array}{rr}Tobin’s Q_{it}=& {\eta }_0+{\eta }_1{discm}_{it}+{\eta }_2{M}_{it}+\gamma {Contorls}_{it}\hfill \\ \hfill & +{Firm}_i+{Year}_t+{\epsilon }_{it}\hfill \end{array}$$

(5)

In this equation, $M_{it}$ are the mediating variables measuring financing constraints, financial risk, and the level of green investment, respectively. Other variables follow the baseline specification.

5.1. ESG Disclosure and Financing Constraints

We adopt the SA index proposed by Hadlock & Pierce (2010) [43] as a proxy variable for corporate financing constraints. The SA index, constructed from two stable and strongly exogenous variables, offers a reliable and stable measure of financing constraints. In regression, we use the absolute SA index value to reflect financing constraint severity. Column (1) of

Table 7. Mechanism Test.

	Mediating Effect of Financing Constraints		Mediating Effect of Financial Risk		Mediating Effect of Green Investors Entry
Variable	(1)	(2)	(3)	(4)	(5)	(6)
	SA	Tobin’s Q	Z-Score	Tobin’s Q	GInv	Tobin’s Q
discm	−0.0945 ***	1.0524 **	2.9449 **	0.6626 ***	0.4770 **	0.9389 ***
	(0.0290)	(0.4024)	(1.3476)	(0.2045)	(0.1871)	(0.3205)
SA		−1.9560 **
		(0.8858)
Z-score				0.1951 ***
				(0.0459)
GInv						0.6255 ***
						(0.1668)
_cons	5.6197 ***	21.8340 ***	7.9309	9.2942 **	−0.6725	11.2623 ***
	(1.4277)	(7.0098)	(12.5376)	(3.5498)	(2.1783)	(3.4101)
Sobel test		0.1849 *		0.5746 *		0.2984 **
Bootstrap test		0.1214 **		0.3429 *		0.2257 **
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm Effect	Yes	Yes	Yes	Yes	Yes	Yes
Year Effect	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	408	408	408	408	408	408
Adj.R-squared	0.7145	0.3168	0.3031	0.6076	0.1172	0.4022

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The numbers in parentheses are firm-level clustered robust standard errors.

reports the regression results of ESG disclosure on the SA index, where the coefficient is significantly negative. Column (2) reveals discm remains significant after controlling for SA index, indicating tourism firms enhance value by alleviating financing constraints through ESG disclosure. Both the Sobel test and Bootstrap test results are significant. The above analysis confirms that ESG disclosure alleviates the adverse effect of financing constraints on tourism firm value, supporting hypothesis H2a.

5.2. ESG Disclosure and Financial Risk

Referencing Altman (1968) [44], we uses the Z-score as the proxy variable for corporate financial risk. The Z-score model integrates five categories of financial indicators: liquidity, profitability, solvency, leverage level and asset turnover efficiency, which can comprehensively reflect the financial health of a company. Column (3) of Table 7 shows the regression results of ESG disclosure on the Z-score, where the coefficient is significantly positive. In column (4), discm remains significant at the 1% level after controlling for Z-score, indicating tourism firms enhance value by reducing financial risk through ESG disclosure. Moreover, both the Sobel test and Bootstrap test results are significant. Based on the above analysis, it can be concluded that ESG disclosure positively impacts the market value of tourism enterprises by reducing financial risk and research hypothesis H2b is supported.

5.3. ESG Disclosure and Green Investors Entry

We construct the green investor entry indicator by matching CSMAR’s “Fund Entity Information Table” and “Stock Investment Details Table” to identify funds investing in tourism companies. Funds with environmental-related terms in their investment objectives or scope are classified as green investors. The variable is measured as the natural logarithm of one plus the number of green investors. In column (5) of Table 7, the regression coefficient of ESG disclosure on green investors is significantly positive. Column (6) shows that discm remains significant after controlling for green investors, indicating ESG disclosure attracts green investors to enhance firm value. Both the Sobel test and Bootstrap test results are significant. Therefore, it can be concluded that ESG disclosure enhances the positive effect of green investors entry on the value of tourism companies and research hypothesis H2c is supported.

6. Heterogeneity Analysis

The preceding analysis confirms that ESG disclosure significantly enhances the market value of tourism enterprises. However, this positive effect may not be uniform across all firms. Differences in corporate ownership nature, governance structure, and other characteristics could lead to varied outcomes. Therefore, this section conducts a heterogeneity analysis to further explore the differential impacts of ESG disclosure under different firm-specific conditions.

6.1. Ownership Nature

Based on the institutional context of China, enterprises with different ownership natures may differ in their ability to access resources and policy response motives, thereby differentially moderating the effect of ESG disclosure on tourism firm value (Siwei et al., 2023) [45]. We divide the sample companies into state-owned and non-state-owned enterprises to examine the differences in the effects of ESG disclosure on market value among tourism enterprises with different ownership natures. As shown in

Table 8. Heterogeneity Analysis.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	Tobin’s Q	Tobin’s Q	Tobin’s Q	Tobin’s Q	Tobin’s Q	Tobin’s Q
	Non-SOE	SOE	Separation	Duality	Low GTFP	High GTFP
discm	0.6558	1.5257 ***	1.4429 ***	−0.0941	2.5817 ***	0.3943
	(0.5008)	(0.4979)	(0.4274)	(0.3269)	(0.5886)	(0.2483)
_cons	12.3491 ***	17.8325 **	13.6271	12.7218	11.5573	15.9676 **
	(3.8092)	(7.7277)	(8.1401)	(11.7010)	(12.9147)	(7.1502)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Firm Effect	Yes	Yes	Yes	Yes	Yes	Yes
Year Effect	Yes	Yes	Yes	Yes	Yes	Yes
Obs.	126	282	329	79	198	197
Adj.R-squared	0.3500	0.3211	0.2939	0.5371	0.3562	0.4304

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The numbers in parentheses are firm-level clustered robust standard errors.

, the coefficient on discm for the state-owned enterprise sample is 1.5257 and significant at the 1% level in Column 1. In contrast, the coefficient is much smaller at 0.6558 and statistically insignificant for the non-state-owned enterprise subsample in Column 2. This suggests that state-owned tourism enterprises, likely due to greater policy support and a stronger mandate for social responsibility, see a more pronounced market impact from their ESG disclosure.

6.2. CEO Duality

The concurrent positions of chairman and CEO (Duality) or the separation of these positions (Separation) are core institutional arrangements. Duality may lead to excessive concentration of managerial power and exacerbate agency conflicts (Jensen, 1993) [46]; whereas separation can enhance decision-making transparency and constrain opportunistic behavior (Fama & Jensen, 1983) [47]. To examine the heterogeneous characteristics of the concurrent or separate positions of chairman and CEO in the impact of ESG disclosure on market value, we divide the sample into two groups based on duality or separation for analysis. In Column (3) of Table 8, the coefficient on discm for the separation sample is 1.4429 and significant at the 1% level. By contrast, for the duality sample in Column (4), the coefficient on discm is statistically insignificant. This indicates that in companies where the positions of chairman and CEO are separated, ESG disclosure more significantly enhances corporate value.

6.3. Environmental Efficiency

Driven by the “dual carbon” goals, Green Total Factor Productivity (GTFP) has become a key indicator for evaluating corporate environmental governance effectiveness and sustainable development potential (Li et al., 2023) [48]. Following the approach of Sun Yannan et al. (2021) [49], we use a super-efficiency SBM model including undesirable outputs to measure GTFP. The calculation of GTFP mainly includes input and output aspects: input variables include capital, labor and energy, with expected output being operating revenue, and undesirable outputs covering carbon dioxide and wastewater emissions. Table 8 reveals that the positive effect of ESG disclosure on firm value is concentrated in low-GTFP tourism enterprises, where the coefficient on discm is significant at the 1% level. This effect, however, disappears for high-GTFP firms. This suggests that for companies facing significant pressure for green transformation, ESG disclosure can effectively offset the negative market expectations arising from insufficient green production efficiency.

6.4. Tourism Sub-Industries

According to the “National Classification of Tourism and Related Industries (2018)” issued by the National Bureau of Statistics of China, the tourism industry includes 7 major categories (tourism transportation, tourism accommodation, tourism catering, tourism sightseeing, tourism shopping, tourism entertainment, and tourism comprehensive services). Based on this, we further divide the selected listed tourism enterprises into four sub-industries: accommodation and catering, tourism transportation, tourism sightseeing, and tourism comprehensive services.

As shown in

Table 9. Tourism Subsector Analysis.

Variable	(1)	(2)	(3)	(4)
	Accommodation and Catering	Tourism Transportation	Tourism Sightseeing	Tourism Comprehensive Services
discm	1.4651 ***	0.3889	1.2303 ***	2.1750
	(0.3128)	(0.2281)	(0.1824)	(1.4398)
_cons	37.3722 **	−12.1873	15.0477 **	14.4567 **
	(13.6309)	(20.8227)	(5.4986)	(5.2187)
Controls	Yes	Yes	Yes	Yes
Firm Effect	Yes	Yes	Yes	Yes
Year Effect	Yes	Yes	Yes	Yes
Obs.	68	87	125	128
Adj.R-squared	0.7469	0.3507	0.5962	0.2930

Note: *, **, and *** denote significance at the 10%, 5%, and 1% levels, respectively. The numbers in parentheses are firm-level clustered robust standard errors.

, the significantly positive coefficient for accommodation and catering enterprises indicates that ESG disclosure enhances their market value. As this sub-industry directly faces consumers, its ESG practices such as green hotel certifications and food safety management are easily perceived by the market and can be converted into brand premiums. For tourism transportation enterprises, the coefficient of ESG disclosure does not pass the significance test, meaning the role of ESG disclosure in enhancing the value of such enterprises is limited. For tourism sightseeing enterprises, ESG disclosure significantly boosts market value, likely because their practices (e.g., ecological protection and cultural heritage management) align with policy guidance, attracting government subsidies and tourist recognition. The coefficient of ESG disclosure of tourism comprehensive service companies is not significant, and their diversified business segments may lead to ESG disclosure being difficult to be effectively decoded by the market due to excessive information complexity.

7. Discussions and Conclusions

7.1. Main Findings and Contributions

This study mainly empirically examines the relationship between ESG and tourism firm value. Our findings demonstrate that ESG disclosure significantly enhances the value of tourism companies, a result that holds across a series of robustness checks. Mechanism analysis indicates that ESG disclosure enhances the value of tourism enterprises by alleviating financing constraints, reducing financial risk, and attracting green investors. Heterogeneity analysis shows that the promoting effect of ESG disclosure on market value is more pronounced in SOEs, firms with non-duality and low GTFP groups. In tourism sub-industries, the value-enhancing effect of ESG disclosure is more evident in accommodation and catering companies and tourism sightseeing companies.

Our findings corroborate prior research while highlighting the unique dynamics of the tourism industry. A key mechanism is the attraction of green investors, demonstrating that for tourism firms, ESG is a strategic tool for securing niche capital and enhancing value, given their reliance on brand and environmental image. Furthermore, we reveal significant intra-industry heterogeneity in ESG’s effects, underscoring that its effectiveness is context-dependent. Our findings thus provide differentiated, practical guidance for ESG implementation within the tourism sector.

7.2. Policy and Managerial Implications

Based on these findings, we propose the following policy recommendations:

First, as China’s ESG rating system for tourism enterprises is still in its formative stage, government-led strategic guidance is crucial for strengthening the top-level ESG framework. This involves two key actions: accelerating the development of a unified ESG institutional framework with robust disclosure standards, and enhancing oversight of rating agencies to curb “greenwashing.” Given the tourism industry’s high sensitivity to natural resources and consumer trust, formulating industry-specific ESG evaluation standards is also recommended.

Second, tourism enterprises should shift from passive to proactive ESG reporting, a key driver of innovative development. This requires recognizing that strategic investments in ESG areas create long-term value, and understanding the mechanisms through which disclosure enhances firm value. Such strategies are particularly crucial for state-owned tourism enterprises, companies with separated leadership roles, those with low green total factor productivity, accommodation and catering, and sightseeing companies, as they derive the greatest benefits from ESG practices.

Third, integrating ESG principles is crucial for guiding tourism investment, as non-financial performance has become a key driver of industry sustainability. While ESG is gaining traction, a wait-and-see attitude persists among some investors. Therefore, a combination of government regulation and market incentives is needed to steer investors toward the ESG performance and long-term value of tourism companies. Furthermore, encouraging investors to actively engage in corporate ESG governance can optimize the internal management of these enterprises.

7.3. Limitations and Future Research

Despite its contributions, this study has limitations that offer avenues for future research. First, our analysis is confined to publicly listed tourism companies in China; future studies could extend the sample to include private firms or tourism companies in other countries for cross-market comparisons. Second, we use a composite ESG score; future research could explore the differential impacts of the individual E, S, and G pillars to provide more granular insights.

7.4. Conclusions

In summary, governments, enterprises, and investors should work together to promote tourism enterprises to transform ESG from a passive compliance behavior into an active strategic orientation, using disclosure to drive improvement, and building a virtuous cycle of “policy guidance—ESG quality improvement—value leap” to inject lasting momentum into the high-quality development of the tourism industry, bolstering China’s “dual carbon” goals and advancing global sustainable development.