Industry Heterogeneity and the Economic Consequences of Corporate ESG Performance for Good or Bad: A Firm Value Perspective

Abstract

An investigation into the relationship between ESG performance and firm value is vital for formulating corporate sustainability strategies. This paper begins by providing a comprehensive overview of the ESG performance across all listed companies in the Chinese stock market. It then examines the effect of a firm’s ESG performance on its firm value, with a particular focus on the heterogeneity within various industries. Our results demonstrate that ESG performance standards are positively correlated with the firm value. Enhancements in ESG performance can significantly bolster a firm’s sustainability. Nevertheless, the degree and direction of the impact of corporate ESG performance on firm value are subject to variation across industries. These results have significant implications for the refinement of corporate ESG practice initiatives and ESG-oriented investors, inspiring them to consider the industry classification of firms in their operational and investment strategies related to ESG.

1. Introduction

ESG refers to environmental, social, and governance issues that can influence corporate economic consequences, either positively or negatively. It serves as a critical tool for assessing the sustainability and social impact of corporate operations [1]. Since 2004, with the continuous improvement of disclosure standards, indicator systems, and rating methodologies, ESG criteria have gained widespread adoption in practice. The economic consequences of ESG and its relationship with corporate sustainability have garnered extensive scholarly interest. The mainstream literature on ESG can be distilled into three aspects: the theoretical foundation and the development of rating systems [2,3,4,5,6,7], ESG’s role in risk prevention [8,9,10], and the exploration of its economic consequences [11,12,13,14,15]. The methodologies for ESG ratings are currently undergoing continuous refinement. Additionally, ESG is often regarded as a form of non-financial investment by companies, serving as a mechanism for risk prevention. Although ESG is thought to bring a multitude of benefits across the economic, environmental, social, and governance dimensions, it represents the investment concept of sustainable development and the pursuit of long-term value growth. Nonetheless, there is an ongoing debate about whether ESG practices can enhance corporate economic consequences [1,16].

Regarding the controversy over the economic consequences of ESG practices, whether beneficial or detrimental, some studies have indicated that potential contributing factors may include the corporate ownership structure, the influence of institutional investors, and the level of attention given to ESG issues [17,18,19]. Furthermore, identifying other factors that affect the economic consequences of ESG practices is an important area that merits further research. Figure 1 presents a comparative bar chart analysis depicting the average ESG scores alongside firm values across different industries in China. The upper section of the chart demonstrates that most industries have ESG scores between 40 and 50. In contrast, the lower section of the chart exposes a substantial disparity in firm values, with the coal industry emerging as the leader at nearly 2000 billion RMB, followed by the food industry. The majority of other industries exhibit significantly lower firm values in comparison. Notably, industries such as media and financials boast higher median ESG scores, which suggests a superior overall ESG performance. Conversely, sectors including chemicals, defense, and steel are marked by lower scores, indicating a need for improvement in their ESG practices. An observation drawn from Figure 1 is that while there is a pronounced variance in average firm values among industries, the variance in average ESG scores is comparatively modest.

Figure 2 offers a scatter plot that elucidates the correlation between the average ESG scores and the average firm values for listed companies across industries in China. Within sectors such as retail, machinery, electrical, and construction, a positive correlation is evident, which suggests that companies in these fields may augment their market value through concerted efforts to improve their ESG performance. In contrast, the coal and steel industries exhibit a negative correlation, with the steel industry showing a notably steep negative slope, indicating a potential inverse relationship between heightened ESG scores and firm value. According to Figure 2, the correlation between ESG scores and firm value is not uniform and varies in magnitude and direction among different industries. This diversity is likely influenced by a multitude of factors, including the unique characteristics of each sector, market perceptions, the regulatory environment, and the specific ESG strategies implemented by companies within these industries.

The preceding analysis indicates that the industry classification of firms has a significant impact on the economic consequences of their ESG practices. This paper uses data from Chinese listed companies to delve into the relationship between ESG performance and firm value and then examines the heterogeneous effects (especially industry classification) of ESG performance on firm sustainability. Our paper contributes to the existing literature on the economic consequences of ESG by providing a favorable explanation for the mixed economic consequences of ESG performance. In addition, our findings suggest that firms should consider the unique characteristics of their industry when formulating ESG strategies to achieve sustainable growth and long-term value creation.

The structure of this paper is as follows: Section 2 is the literature review. Section 3 elaborates on the research design and model construction. Section 4 showcases the empirical results, accompanied by robustness tests to validate the reliability of our findings. Section 5 discusses our research. Finally, Section 6 concludes the paper by offering insights and proposing avenues for future inquiry.

2. Literature Review

Firm value serves as a pivotal indicator, reflecting a company’s prospective growth potential, governance proficiency, and risk management acumen—factors essential for its enduring sustainability [20,21,22,23,24]. The topic of the impact of ESG performance on economic consequences is relatively rich. Numerous mechanisms have been identified, through which ESG performance can influence firm value. Fatemi et al. [25] examine the impact of ESG activities on firm value. They find that ESG strengths increase firm value, while weaknesses decrease it. Xie et al. [26] propose that ESG activities exhibit a positive relationship with corporate financial performance, providing evidence that voluntary corporate social responsibility strategies can enhance corporate sustainability. Wong et al. [27] demonstrate that ESG certification can reduce a firm’s capital costs and notably enhance Tobin’s Q. Superior ESG performance is also posited to curtail both capital and financing expenses, thereby instilling a positive influence on firm performance and valuation [28,29]. Edmans [30], through empirical scrutiny of the correlation between firm stock returns and ESG performance, uncovers that those entities excelling in ESG performance command higher market valuations. Lian [31] illustrates that robust ESG performance can ameliorate corporate investment inefficiencies by galvanizing market scrutiny, significantly attenuating performance volatility, and thereby enhancing corporate investment efficiency.

Conversely, a subset of studies suggests that overemphasizing a company’s ESG performance and associated investments might precipitate resource allocation inefficiencies and investment misalignments, potentially diminishing firm performance and value [32]. Moreover, in the pursuit of enhanced ESG performance, companies may incur increased production costs and augmented related investments, which could adversely affect firm performance and value [33,34]. Many scholars have posited that there exists no significant correlation between ESG performance and firm value, challenging the prevailing view and indicating the complexity of this relationship [35,36].

The firm value represents a pivotal indicator of a company’s capacity to sustain its operations over time. According to existing studies, ESG can enhance firm sustainability through mechanisms such as reducing financing costs, increasing the value of intangible assets, and improving corporate governance quality. Friede et al. [16] analyzed more than 2000 empirical studies and found that roughly 90% of these studies report a positive ESG–CFP relationship. It is therefore necessary to ascertain why good ESG performance increases firm value. Legitimacy theory posits that firms need to maintain their societal legitimacy by aligning their operations with societal norms, values, and expectations [37,38,39,40,41]. The literature in this area often examines how ESG disclosures and practices can lead to improved firm reputation, reduced regulatory scrutiny, and increased firm value [42,43,44]. Stakeholder theory suggests that firms should create value for all stakeholders, not just shareholders, by considering the needs and interests of employees, customers, suppliers, communities, government and the environment [42,45]. Research from this perspective typically explores the impact of ESG initiatives on stakeholder satisfaction, which can, in turn, affect a firm’s financial performance and long-term viability [40,41]. Signaling theory focuses on the information asymmetry between firms and external parties [46]. Studies within this framework often analyze how ESG disclosures can reduce information asymmetry, influence investor perceptions, and potentially affect stock prices, cost of capital, and firm value [18,36].

Based on the existing studies, the majority of the literature concludes that ESG performance has a positive impact on firm value. However, there remains controversy surrounding the relationship between ESG and firm value. Besides the traditional theories, what are the reasons why some studies find a negative relationship between ESG and firm value? This paper provides a more intuitive explanation from a heterogeneity perspective.

3. Data and Methodology

3.1. Data Selection and Description

This research includes ESG score data and financial data for all A-share listed companies, with the data sourced from the WIND database. The sample period spans from December 2018 to September 2023, considering the late disclosure of ESG data.

This paper is designed to explore the impact of ESG performance on the sustainable development of Chinese companies. Based on the aforementioned analysis, firm value is deemed an appropriate metric for assessing the capacity for sustainable growth and the potential for long-term value creation. The dataset encompasses ESG performance scores and corresponding firm value data, denominated in billions of RMB, for all listed companies sourced from the WIND database. The ESG score serves as an indicator of a company’s adherence to environmental, social, and governance criteria, while firm value, as derived from financial statements, signifies the comprehensive valuation of all the company’s assets, with the exclusion of cash and cash equivalents.

Table 1 delineates a marked escalation in the mean ESG score, escalating from 42.98 in December 2018 to 49.86 by September 2023, which signifies a general enhancement in corporate ESG performance throughout the observed period. The median scores parallel this upward trajectory, indicating a sustained trend in improvement. Variability in firm values is noted, with a zenith recorded in December 2021. Industry-wise, the food sector claims the highest mean ESG score at 56.67, contrasting with the chemicals sector’s lowest score of 39.85. Notably, the coal industry boasts the most substantial mean firm value, reaching 2007.84 billion RMB. These observations highlight an escalating focus on ESG metrics alongside a spectrum of financial performances across different sectors.

To delve deeper into the nuances of average ESG scores among various industries, a box-and-line plot has been constructed and is presented in Figure 3. This visual representation elucidates the discernible disparities in the distribution of ESG scores across the spectrum of industries, underscoring the heterogeneity in ESG performance.

3.2. Research Hypothesis and Model Construction

As one of the most critical attributes of listed companies, does industry classification affect the heterogeneity in the relationship between ESG and firm value? In pursuit of a deeper understanding of the interplay between the ESG practices of Chinese listed companies and their sustainability capabilities, as well as the potential variability of this relationship across different industries, we articulate the following two hypotheses for examination:

Hypothesis 1.

The aggregate impact of ESG performance on firm value is anticipated to be positive within the Chinese market.

Hypothesis 2.

The impact of ESG performance on firm value exhibits heterogeneity across various industry classifications.

This paper adopts firm value as the dependent variable (Firm value), and the ESG score as the independent variable (ESG) to investigate the influence of ESG performance on a company’s sustainable development in China. We employ a panel data variable coefficient model, denoted as Model 1, which is articulated in Equation (1). Firm growth is subject to a multitude of determinants, including profitability. Consequently, Model 1 incorporates control variables, represented by $X_t$, to account for these factors. These control variables encompass market value and the growth rate of earnings per share (EPS), return on equity (ROE), operating income, and net profit. Market value is an important gauge of a firm’s growth potential, although not a dependable indicator of sustainable growth or the creation of long-term value.

(1)$Firm Valu{e}_t=\alpha +\beta \times ES{G}_t+\gamma \times X_t+\epsilon$

To delve deeper into whether enhancements in ESG performance are associated with an increase in a firm’s enterprise value, we construct an incremental model depicted in Equation (2). In this model, the independent variable is the change in firm value, represented as $∆ Firm Valu{e}_{[t,t+1]}=Firm Valu{e}_{t+1}-Firm Valu{e}_t$, and the dependent variable is the change in ESG Score, represented as $∆ ES{G}_{[t,t+1]}=ES{G}_{t+1}-ES{G}_t$. Equation (2) also includes a set of control variables, denoted by $X_t$, to adjust for other factors that may influence the relationship between the variables.

(2)$∆ Firm Valu{e}_{[t,t+1]}=\alpha +\beta \times ∆ ES{G}_{[t,t+1]}+\gamma \times X_t+\epsilon$

In light of the differences among firms, we consider the individual fixed effects when constructing the model, controlling for unobservable heterogeneity. The fixed effects (FE) models of (1) and (2) are depicted as Equations (3) and (4).

(3)$Firm Valu{e}_t={\alpha }_i+\beta \times ES{G}_t+\gamma \times X_t+\epsilon$

(4)$∆ Firm Valu{e}_{[t,t+1]}={\alpha }_i+\beta \times ∆ ES{G}_{[t,t+1]}+\gamma \times X_t+\epsilon$

4. Empirical Results and Analysis

4.1. The Overall Effect Analysis

In the empirical study, we use Model 1 as the benchmark for empirical analysis. Table 2 shows the regression results of the model. Specifically, regression (1) in Table 2 displays the results from the Pooled OLS regression. Building upon this, regression (2) introduces control variables that quantify the firms’ growth potential (EPS growth rate, net profit growth rate, ROE growth rate, operating income growth rate). To further explore the influence of firms’ heterogeneous effects on the regression outcomes, regression (3) incorporates firm fixed effects. Regression (4) refines the analysis by simultaneously controlling for individual effects and incorporating the aforementioned control variables.

The empirical results presented in Table 2 provide a comprehensive analysis of the relationship between ESG scores and firm value across different model specifications. In regression (1) and (2), ESG scores exhibit a significantly positive effect on firm value, with coefficients of 24.716 and 24.722, respectively, both significant at the 1% level. Regressions (3) and (4), which introduce firm fixed effects but exclude market value (MV) as a control, show a reduced but still significant positive impact of ESG scores, with coefficients of 3.942 in both cases. When incorporating market value in regressions (5) and (6), the results indicate a negative and significant relationship between ESG scores and firm value, with coefficients of −1.362 and −1.356, respectively, and both models exhibit a substantial increase in R-squared values to 0.4763, suggesting a better fit. Notably, the inclusion of control variables in these models further underscores the robustness of the results. Regressions (5) and (6) demonstrate the reversed effects of ESG scores when accounting for firm-specific factors and market value. Based on the above analysis, the findings highlight the complex relationship between ESG performance and firm value. However, regression (7) has demonstrated that this complex relationship is related to individual firm heterogeneity. The result reveals a positive and significant relationship between ESG scores and firm value, with a coefficient of 0.584 and an R-squared value of 0.4489.

Upon a comprehensive analysis of the regression results presented in Table 2, we can conclude that in China, the ESG performance of listed companies has a positive impact on their sustainable development after controlling for differences among firms, therefore substantiating Hypothesis 1 as valid. However, to elucidate the underlying drivers of the intricate relationship between ESG performance and firm value, further analysis is warranted.

4.2. The Heterogeneity Effect Analysis

Our research verifies that ESG performance has a positive impact on companies’ sustainability, which is in line with 90% of the literature that has come to the same conclusion. However, why are there still many studies indicating that corporate ESG performance is not correlated or negatively correlated with its sustainability? In the Introduction section, Figure 2 initially concluded from the statistical data that the relationship between ESG performance and the firm value of listed companies belonging to different industries is significantly different in China. Several factors may contribute to the observed differences in the impact of ESG practices on operating costs and financing costs across different industries. For instance, the cost of ESG inputs in industries with high levels of pollution is considerably higher than in other industries. The following section presents a rigorous examination of research hypothesis 2, which seeks to determine whether industry heterogeneity is responsible for the inverse relationship between ESG performance and firm value.

According to the classification of first-tier industries, all A-share listed companies can be categorized into 30 groups. Then, we use ANOVA to test the difference between the average ESG performance and the average firm value of different industries. Figure 4a shows the heatmap for the analysis of the intergroup variability in ESG scores and Figure 4b shows the heatmap for the analysis of the intergroup variability in firm value. Based on Figure 4, it can be seen that there is a significant difference in the average ESG performance of different industries; on the contrary, there is no significant difference in firm value between most industries.

To delve deeper into the influence of industry heterogeneity on the nexus between ESG and firm sustainability, we introduce interaction terms between industry categories and ESG scores into Model 1 for regression analysis. The regression results are shown in Table 3. Specifically, regression (1) in Table 3 reflects the Pool OLS outcomes of ESG performance on firm value, adjusted for industry effects. The regression coefficient for ESG is significantly positive; however, there is notable heterogeneity in the impact of ESG performance on firm value across industries, with several industries (e.g., coal, steel) demonstrating significant negative coefficients for the interaction terms. In regression (2), upon incorporating market value, the influence of ESG performance on firm value reverses from positive to negative, with industry heterogeneity persisting, as indicated by both the positive and negative regression coefficients for the interaction terms. Regression (3) incorporates additional control variables and accounts for individual effects. The results indicate that while ESG performance positively affects firm value, its regression coefficient becomes insignificant (T = 0.8334), yet industry heterogeneity persists.

As the regression results in (3) of Table 3, after controlling for firm-specific effects and other variables, significant heterogeneity in the impact of ESG performance on firm value is observed within six key industries: coal, finance, food, petroleum, construction, and transportation. Consequently, we have extracted data from all listed companies within these industries to create a sub-sample, enabling a focused investigation of the heterogeneous impact of ESG performance on firm value across different industries. The findings are presented in Table 4.

In Table 4, Sub_ALL indicates the regression results for the subsample Pool OLS, and Coal and others are the empirical results of the regressions on the corresponding industry data, respectively.

Based on Table 4, it can be seen that the effect of ESG scores on firm value varies significantly across industries. While ESG is positively associated with firm value in the overall sample and construction industry, it shows negative impacts in the coal and petroleum sectors. Market value consistently exhibits a strong positive impact across all industries. In the Sub_ALL sample, the ESG scores exhibit a positive albeit statistically insignificant coefficient (1.6967). In contrast, in the coal industry, ESG scores have a significantly negative impact on firm value (−2.2044 **), indicating potential sector-specific challenges or costs associated with ESG practices. The petroleum industry also exhibits a negative and significant relationship (−6.6874 **), further substantiating the potential adverse effects of ESG in extractive sectors. Conversely, the construction industry demonstrates a markedly positive and significant coefficient for ESG scores (4.4150 ***), suggesting that ESG initiatives may be particularly beneficial or better integrated within this sector. The food industry, while exhibiting a positive coefficient, does not achieve statistical significance (0.0981), indicating a less clear impact of ESG practices. Market value (MV) consistently demonstrates a robust, positive, and highly significant relationship with firm value across all sectors, with coefficients ranging from 0.2589 in the financial industry to 0.9321 in the food industry. The R-squared values indicate the varying explanatory power of the models, with particularly high values observed in the food (0.9181) and coal (0.7174) industries.

In conclusion, these results demonstrate the heterogeneous effects of ESG practices across different industries, emphasizing the importance of contextual and sector-specific analyses in understanding the broader implications of ESG initiatives on firm value.

4.3. Robustness Test

To substantiate the robustness of the findings presented in this paper, we perform a robustness check utilizing Model 2. In this model, we calculate the growth rates of the ESG score, market value, and firm value, respectively. This approach allows us to assess whether the improvement of ESG performance contributes to the increase in firm value. Table 5 displays the regression outcomes of Model 2, which indicate that improvements in ESG performance significantly bolster firm sustainability. The regression coefficients of the change in ESG (ΔESG) on firm value across specifications (1) to (7) are consistently and significantly positive. Consequently, the initial research conclusion of this paper is confirmed as robust. Firms have the potential to augment their sustainability by investing in ESG practices and elevating their ESG performance.

Furthermore, addressing the second conclusion of this study—that industry heterogeneity influences the effect of ESG performance on corporate sustainability—we have also conducted a robustness test using Model 2. The regression results are shown in Table 6. Within Table 6, “Sub_ALL” corresponds to the regression outcomes for the pooled OLS subsample, while “Coal”, “Finance”, “Food” and “Construction” represent the empirical results from regressions conducted on data specific to these industries. The enhancements in ESG performance for companies within the coal, finance, and food sectors are found to have a significant positive impact on their firm value, with regression coefficients of 0.4093, 0.2799, and 0.6996, respectively. Conversely, the ESG practices of companies in the construction industry are associated with a significantly negative effect on their sustainability.

Overall, the outcomes of the robustness tests corroborate the robustness of the conclusions reached within this paper. Within the Chinese stock market, the aggregate impact of ESG performance on the firm value of listed companies is affirmative. Furthermore, industry heterogeneity emerges as a significant factor that influences both ESG performance and the sustainability of corporations.

5. Discussion

This paper employs data from Chinese listed companies to investigate the relationship between ESG performance and firm value, and to examine the heterogeneous impact of ESG performance across different industries. Consistent with the findings of 90% of the existing research, we conclude that the overall impact of ESG practices on corporate sustainability is positive. However, the magnitude and direction of this impact are closely related to the industry classification of the listed companies.

The existing studies have provided a theoretical basis for the positive economic consequence of ESG performance, based on legitimacy theory, stakeholder theory, signaling theory, etc. However, the controversy surrounding the relationship between ESG performance and firm value remains unclear. Studies related to shareholding structure and excessive attention have attempted to explain this controversy, but there is no consensus on the impact of this heterogeneity. This paper presents a more nuanced explanation from a heterogeneity perspective.

In our empirical analysis, we initially observed a positive correlation between ESG performance and firm value, as indicated by the regression coefficients from mixed panel regressions. However, once we controlled for individual effects, these coefficients transitioned to negative. This shift suggests that individual heterogeneity could be pivotal in reconciling the disparate views within the literature on the economic repercussions of corporate ESG initiatives. Additionally, our study reveals that industry classification significantly influences the variability of economic outcomes linked to ESG practices. This insight serves as a generalized reference for comprehending the diverse economic implications of firms’ ESG performance, offering a benchmark for subsequent academic and professional discourse.

Our research posits that firms should tailor their ESG strategies to reflect the distinctive attributes of their respective industries to enhance sustainable growth and foster long-term value creation. By extension, our study contributes to the existing literature on the economic consequences of ESG performance by providing a favorable explanation for the controversial economic consequences of ESG performance. Furthermore, it provides more generalized recommendations for the implementation of firms’ ESG practice plans.

6. Conclusions

The influence of ESG on firm value is a current and prominent topic. Our analysis confirms that industry heterogeneity significantly influences the relationship between ESG performance and firm value, and this finding is robust. This not only enhances the understanding of the economic consequences of ESG performance but also provides theoretical guidance for implementing ESG initiatives.

According to our findings, ESG performance in general has a positive impact on promoting firms’ sustainable growth and long-term value creation. However, the relationship between ESG scores and firm value is not uniform, varying in strength and direction across different industries. This suggests that different firms should focus on the classification of the industry in which they operate when formulating their ESG practice plans. In addition, our findings also indicate to investors who seek long-term value-creation gains that ESG performance is not always a favorable evaluation metric and should be treated distinctly when going across different industries.

The research conclusions drawn in this paper are robust, but there are still some limitations. On the one hand, the sample of our study is drawn from listed firms in China, and whether the findings are transferable globally deserves further research. On the other hand, our study provides evidence of the heterogeneous impact of ESG performance on firm value from an industry perspective, which does not fully resolve the controversy of existing studies on the beneficial economic impact of ESG performance. Future studies could continually explore the heterogenous impact from other perspectives, like regions and policies, etc.

Author Contributions

Conceptualization, Y.C. and Z.Z.; Methodology, Y.C.; Software, Y.C.; Validation, Y.C.; Formal analysis, Y.C.; Investigation, Y.C.; Resources, Y.C.; Data curation, Y.C.; Writing—original draft, Y.C.; Writing—review & editing, Y.C. and Z.Z.; Visualization, Y.C.; Supervision, Y.C.; Project administration, Y.C.; Funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available on request from the authors.

Conflicts of Interest

Author Zili Zhang was employed by the company Harvest Fund Management. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

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Figures and Tables

Figure 1. The bar chart depicts the average ESG scores and firm values across various industries in China. The x-axis categorizes the industries, while the y-axis presents the respective average ESG scores and firm values on two separate scales. This visualization allows for a comparative analysis of both ESG performance and financial standing within each industry sector.

Figure 2. The scatter plot of the average ESG scores and average corporate values of listed companies across different industries in China.

Figure 3. Boxplot distribution of ESG scores across different industries. This boxplot delineates the distribution of ESG scores among various industries. The x-axis categorizes the industries, while the y-axis represents the ESG scores, providing a visual summary of the dispersion and central tendencies of ESG performance within each sector.

Figure 4. The heatmap of inter-industry heterogeneity analysis using ANOVA. The null hypothesis can be rejected when the p-value of the test is less than 0.1, which is considered to indicate that there is a significant difference between the two groups of data. The smaller the p-value is, the bigger the difference between the two groups of data. (a) Analysis of ESG performance. (b) Analysis of firm value.

Table 1

Descriptive statistics of ESG score and firm values across industries and time periods.

	ESG Score					Firm Value
Panel A: Descriptive Statistics of ESG Score and Firm Values across Different Years
Trade_dt	Count	Mean	Min	50%	Max	Count	Mean	Min	50%	Max
2018/12	3386	42.98	20.08	42.23	70.10	3386	154.19	5.14	44.17	55,358.87
2019/12	3575	43.74	17.39	43.04	71.69	3575	193.53	1.19	53.88	70,278.27
2020/12	3982	45.00	23.84	44.25	73.31	3982	229.23	2.56	57.98	78,426.86
2021/12	4509	46.75	20.88	45.97	75.33	4509	233.02	5.03	66.48	78,015.12
2022/12	4908	49.88	27.57	49.15	76.58	4908	193.75	0.99	54.19	81,128.74
2023/09	5035	49.86	26.55	49.13	76.67	5035	188.08	1.58	54.37	56,537.84
Panel B: Descriptive Statistics of ESG Score and Firm Values across Different Industries
RE	2368	47.38	27.16	46.19	75.33	2368	327.72	3.41	114.69	4068.56
Pharma	2515	43.03	23.13	41.83	76.58	2515	99.26	2.56	57.01	1402.01
IT	2227	48.22	29.70	47.68	71.86	2227	408.68	5.97	118.63	8583.36
Conglomerates	1873	42.64	23.11	42.11	66.88	1873	174.15	3.81	56.85	5993.00
Retail	7376	45.00	23.49	43.87	73.28	7376	168.77	1.84	71.43	5834.69
Machinery	1997	44.71	23.71	43.84	69.02	1997	119.62	0.35	53.30	4992.75
Electrical	1915	48.41	32.25	48.02	70.85	1915	159.33	7.60	84.25	1559.17
Construction	6869	49.02	25.29	49.13	70.13	6869	107.23	2.68	48.24	3881.71
Building Materials	1377	47.22	30.11	45.31	70.54	1377	200.70	0.29	44.97	6730.20
Appliances	1537	49.34	31.64	49.26	72.15	1537	149.10	2.65	53.44	2421.10
Automobiles	2800	45.07	21.62	43.95	66.78	2800	220.90	0.99	43.76	6997.09
Textiles	2253	45.36	25.56	45.42	67.28	2253	335.93	3.64	119.08	4754.83
Agriculture	2381	49.17	24.70	49.62	73.56	2381	210.02	4.35	93.59	4298.14
Electronics	8343	44.32	22.38	43.70	69.95	8343	77.14	1.60	38.55	3002.97
Utilities	4253	44.97	24.35	44.04	71.41	4253	132.72	1.19	42.01	8900.67
Telecom	696	51.43	26.55	51.66	71.53	696	345.71	10.80	133.97	4727.29
Environment	1852	46.86	24.19	46.42	65.24	1852	85.07	1.94	47.57	668.21
Petroleum	4732	45.50	25.18	44.12	76.25	4732	194.53	0.69	61.47	13,355.10
Metals	6288	44.56	24.17	42.92	75.03	6288	171.29	2.60	62.67	4686.55
Transportation	776	49.68	28.32	50.35	68.18	776	763.65	6.08	52.56	16,796.42
Media	1198	41.76	24.24	40.70	72.15	1198	106.61	0.95	38.47	5878.22
Financials	1914	44.07	24.80	43.25	68.50	1914	58.59	2.52	36.43	1015.48
Chemicals	692	39.85	17.39	38.47	68.45	692	76.52	0.67	40.41	1413.91
Services	410	47.70	30.51	46.49	70.76	410	154.90	13.71	54.31	1672.88
Light Manufacturing	5244	44.02	20.88	42.60	72.21	5244	122.67	2.74	52.01	5832.05
Defense	2424	46.39	28.32	46.21	68.45	2424	75.96	1.71	39.25	1202.08
Cosmetics	2044	45.17	20.08	43.94	72.89	2044	148.63	1.10	49.10	12,018.09
Food	827	56.67	39.34	56.30	75.40	827	280.14	14.17	159.87	2372.37
Coal	1404	50.59	25.76	49.84	76.79	1404	2007.84	8.37	423.81	81,128.74
Steel	2165	46.81	25.91	46.06	74.29	2165	485.03	4.33	74.06	25,535.79

Table 2

The regression results for Model 1. This table demonstrates the impact of ESG performance on firm value for different listed companies, where (1)–(7) represent whether to add control variables to the baseline model and control for individual effects (√ or ×).

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
ESG	24.716 ***	24.722 ***	3.942 ***	3.942 ***	−1.362 ***	−1.356 ***	0.584 ***
	(47.142)	(47.129)	(20.655)	(20.655)	(−3.433)	(−3.416)	(4.090)
MV					1.452 ***	1.452 ***	0.8161 ***
					(265.21)	(265.21)	(248.63)
Controls	×	√	×	√	×	√	√
Firm FE	×	×	√	√	×	×	√
R2	0.0264	0.0264	0.0055	0.0056	0.4763	0.4763	0.4489

Note: The symbols *** denotes 1% significance level.

Table 3

Regression results for the effect of industry heterogeneity. Regressions (1)–(3) represent whether to add control variables to the baseline model and control for individual effects (√ or ×). In constructing the dummy variables, we set the number of dummy variables to 30 − 1 = 29, and the benchmark is transportation.

	(1)		(2)		(3)
	Parameter	T-Stat	Parameter	T-Stat	Parameter	T-Stat
ESG	23.736 ***	32.74	−1.4356 ***	−2.6167	0.0994	0.8334
MV			1.4261 ***	256.45	0.8156 ***	0.0000
Media_ESG	−3.1714 ***	−4.1424	−1.4053 **	−2.4651	0.3447	0.5632
Utilities_ESG	2.0826 **	2.7888	2.4921 ***	4.482	0.9302	0.1067
Agriculture_ESG	−0.7341	−0.874	−1.2351 **	−1.9749	0.9040	0.1339
Pharma_ESG	−2.2773 ***	−3.7342	−1.8614 ***	−4.0995	0.1902	0.7234
Retail_ESG	−3.2065 ***	−4.0099	−0.6368	−1.0695	0.4261	0.3930
Defense_ESG	−3.8193 ***	−4.9045	−1.9763 ***	−3.4083	0.4125	0.4646
Chemicals_ESG	−5.1927 ***	−8.6132	−0.6075	−1.3523	0.2871	0.5635
Appliances_ESG	−2.1337 **	−2.4679	−2.822 ***	−4.3839	−0.3378	0.6002
Building Materials_ESG	−4.4189 ***	−5.4117	−0.8267	−1.3595	0.5087	0.3737
Construction_ESG	−0.5553	−0.7594	1.3829 **	2.5401	1.0707 *	0.0602
RE_ESG	1.7863 **	2.3283	2.5553 ***	4.4734	0.6345	0.2649
Metals_ESG	−2.9598 ***	−4.0771	−1.1196 **	−2.0712	0.4040	0.4680
Machinery_ESG	−4.0706 ***	−6.7383	−0.8709*	−1.9355	0.4568	0.3607
Automobiles_ESG	−2.7178 ***	−4.0716	−1.8267 ***	−3.6755	0.1255	0.8251
Coal_ESG	−0.8905	−0.8612	−2.941 ***	−3.8201	−2.6396 **	0.0050
Environment_ESG	−4.8184 ***	−6.0169	−0.1347	−0.2258	0.6748	0.2700
Electrical_ESG	−1.5567 **	−2.3926	−2.0963 ***	−4.3276	0.7264	0.1494
Electronics_ESG	−2.0249 ***	−3.2392	−1.7715 ***	−3.8062	0.3644	0.4713
Petroleum_ESG	9.1079 ***	9.0441	−2.6219 ***	−3.4904	3.2701 ***	0.0000
Services_ESG	−2.5326 **	−2.5692	−1.1548	−1.5734	0.2613	0.6401
Textiles_ESG	−4.3821 ***	−5.3433	−0.6389	−1.046	0.1076	0.8666
Conglomerates_ESG	−2.2584 *	−1.7917	−0.266	−0.2834	0.5618	0.2989
Cosmetics_ESG	−3.8068 ***	−2.8116	−1.6157	−1.6027	0.0273	0.9753
IT_ESG	−2.8891 ***	−4.4682	−1.5929 ***	−3.3088	0.3817	0.4759
Light Manufacturing_ESG	−4.9809 ***	−6.6683	−0.5437	−0.9771	0.4241	0.5072
Telecom_ESG	−2.7659 ***	−3.5123	−2.8206 ***	−4.8108	0.2719	0.6975
Steel_ESG	−4.44 ***	−4.8474	−0.3321	−0.4869	0.5945	0.4958
Financials_ESG	38.457 ***	46.698	19.205 ***	31.089	1.6027 **	0.0201
Food_ESG	5.5557 ***	7.2767	−5.9531 ***	−10.44	1.0471 *	0.0884
Controls	×		×		√
Firm FE	×		×		√
R2	0.0801		0.4900		0.4486

Note: The symbols ***, **, and * denote 1%, 5%, and 10% significance levels.

Table 4

Regression results on the impact of ESG performance on corporate sustainability, based on a sub-dataset of six industries. All regressions in this Table consider individual fixed effects and include control variables.

	Sub_ALL	Coal	Financial	Food	Petroleum	Construction	Transportation
ESG	1.6967	−2.2044 **	3.7274	0.0981	−6.6874 **	4.4150 ***	−0.066
	(1.4941)	(−2.593)	(0.561)	(0.110)	(−2.893)	(6.067)	(−0.087)
MV	0.7534 ***	0.6204 ***	0.2589 ***	0.9321 ***	0.7479 ***	0.8175 ***	0.5307 ***
	(59.31)	(39.69)	(3.77)	(147.53)	(30.475)	(16.449)	(23.175)
Controls	√	√	√	√	√	√	√
Firm FE	√	√	√	√	√	√	√
R2	0.2727	0.7174	0.0109	0.9181	0.5607	0.1210	0.2088

Note: The symbols *** and ** denote 1% and 5% significance levels.

Table 5

The regression results of Model 2: robustness testing for Hypothesis 1. The entries (1) to (7) denote the incorporation of control variables and the presence or absence of individual fixed effect controls within the baseline model, indicated by (√) and (×), respectively.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
ΔESG	0.0019 ***	0.0019 ***	0.0055 ***	0.0055 ***	0.0014 **	0.0014 **	0.0033 **
	(3.3468)	(3.4006)	(4.8813)	(4.8971)	(2.4891)	(2.5141)	(2.9916)
ΔMV					0.5302 ***	0.5294 ***	0.5084 ***
					(30.379)	(30.307)	(28.211)
Controls	×	√	×	√	×	√	√
Firm FE	×	×	√	√	×	×	√
R2	0.0001	0.0002	0.0003	0.0004	0.0120	0.0120	0.0113

Note: The symbols *** and ** denote 1% and 5% significance levels.

Table 6

Robustness test for Hypothesis 2: regression analysis of Model 2 using a sub-dataset from six industries. The regressions presented in this table have been adjusted for individual fixed effects and are complemented by the inclusion of control variables.

	Sub_ALL	Coal	Financial	Food	Petroleum	Construction	Transportation
ΔESG	0.0375	0.4093 **	0.2799 ***	0.6996 ***	−1.7634	−0.2604 **	0.0103
	(0.345)	(2.3835)	(3.758)	(6.8603)	(−1.4092)	(−2.2363)	(0.120)
ΔMV	0.6230 ***	0.7966 ***	0.7092 ***	0.6366 ***	0.0865	0.6605 ***	0.552 ***
	(23.931)	(20.051)	(38.180)	(27.007)	(0.2269)	(25.088)	(26.179)
Controls	√	√	√	√	√	√	√
Firm FE	√	√	√	√	√	√	√
R2	0.0603	0.4019	0.5428	0.2935	0.0031	0.2096	0.2583

Note: The symbols *** and ** denote 1% and 5% significance levels.

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