Is the ESG Performance of State-Owned Enterprises Becoming a Pivotal Role?—Based on the Empirical Evidence from Chinese Listed Firms

Abstract

The fundamental principles of “sustainable development” and “green” promoted by ESG align with the concept of “green and sustainable” development. Enhancing enterprise ESG is a methodical endeavor that necessitates enterprises to possess ESG investment capabilities, coordinate many stakeholders, and leverage the influence of prominent market players. State-owned enterprises (SOEs) possess a specific level of support within a nation’s economy. SOEs serve as a fundamental pillar of China’s socialist economic system with distinctive characteristics, significantly influencing business conduct and reinforcing corporate value orientation. Consequently, the capacity of SOEs to assume a strategic leadership role in enhancing supply chain ESG performance is of paramount importance for the general elevation of ESG standards among Chinese enterprises. Limited research has investigated the transmission effect of the ESG performance among chain enterprises from a supply chain viewpoint, particularly regarding the pivotal role of SOEs in enhancing the ESG performance of these entities. This article examines the influence of SOEs’ ESG performance on the ESG performance of supply chain enterprises, focusing on the spillover effects of SOEs’ ESG performance within the supply chain context. It investigates how SOEs lead upstream and downstream enterprises in enhancing their ESG performance, aiming to address the existing cognitive gap in this area and provide substantial evidence for pertinent theories and practices. This article, employing an empirical research methodology, discovers that the ESG performance of state-owned supply chain core enterprises significantly enhances the ESG performance of enterprises in a supply chain, while non-state-owned supply chain core enterprises do not exhibit this effect. Furthermore, research indicates that this effect is asymmetric: when the supply chain core enterprise is a SOE and the enterprises in the supply chain are non-state-owned, the leading effect is more pronounced, and this effect is more powerful for upstream enterprises. The heterogeneity test reveals that the impact of the ESG performance is more pronounced in larger state-owned supply chain core enterprises that have been publicly listed for an extended duration and operate in highly competitive markets. The conclusions of this essay address the deficiencies of current research and provide significant practical implications for the development of green supply chains in the contemporary era.

1. Introduction

Accelerating the green transformation of development methods and actively and consistently advancing carbon peaking and carbon neutrality are essential for promoting green development. On the one hand, ESG’s guiding principles of “green” and “sustainable development” are consistent with the development philosophy of “green sustainability”. On the other hand, improving corporate ESG is a quite methodical endeavor. It demands that businesses coordinate various stakeholders, have the ability to invest in ESG, and have the market leadership of powerful corporations. The question of whether state-owned enterprises (SOEs) can take the lead in strategically raising the supply chain’s ESG performance level is a pressing theoretical one that requires attention because of their supportive position in the national economy.

SOEs are the solid foundation of the socialist economic system with distinctive Chinese features, particularly in China. Chinese SOEs stand out in supply chain interactions for setting an example for downstream and upstream businesses and promoting corporate value orientation [1,2,3]. In light of this, this study investigates the heterogeneity of the effects of SOEs’ ESG performance on the ESG performance of upstream and downstream enterprises from the standpoint of supply chain interactions in the Chinese market. It has important practical ramifications for creating a green supply chain in the modern day, in addition to helping solve the limitations of previous studies.

Existing studies on the financial effects of ESG performance have produced significant findings and are generally consistent [4,5]. Regarding how to enhance ESG performance, there are notable disparities in emphasis, and research in this field is ongoing [4,6]. The transmission effects of the ESG performance among enterprises from a supply chain perspective are particularly poorly explored in the literature, and even fewer studies address the issue of heterogeneity and the leading role of SOEs in improving the ESG performance of chain enterprises. This is an excellent research opportunity for this paper as well. Thus, this article’s study hypothesis is that “the ESG performance of state-owned supply chain core enterprises has a leading role in enhancing the ESG performance of upstream and downstream enterprises with supply chain relationships”. This seeks to close the cognitive gap in the body of knowledge in this area and offer a strong foundation of evidence for related theories and methods.

This article looks at the spillover effects of SOEs’ ESG performance from a supply chain perspective in order to investigate how their ESG performance affects the ESG performance of businesses within the supply chain. The results of the study show the following: the ESG performance of state-owned supply chain core enterprises significantly enhances the ESG performance of enterprises in the supply chain, whereas non-SOEs supply chain core enterprises do not exhibit this influence. This effect is particularly pronounced among non-SOEs in the supply chain and exhibits asymmetry between upstream and downstream supply chain entities. The heterogeneity test reveals that the impact of the ESG performance is more pronounced in larger state-owned supply chain core enterprises that have been publicly listed for an extended duration and operate in highly competitive markets.

The following are this article’s theoretical contributions: First, it expands the research on the factors influencing the improvement of enterprise ESG performance. This article creatively explores the factors affecting the improvement of the ESG performance in enterprises in the supply chain, focusing on the leading role of state-owned supply chain core enterprises, thereby extending existing research. Second, it further enriches the related research on SOEs. By segmenting the equity nature of enterprises in the supply chain, this article fully explores the leading effect and heterogeneity of state-owned supply chain core enterprises in enhancing the ESG performance of enterprises in the supply chain. Third, it reveals the asymmetrical characteristics of the ESG leading effect of state-owned supply chain core enterprises. This article finds that the leading effect of the ESG performance of state-owned supply chain core enterprises exhibits asymmetry between upstream and downstream enterprises in the supply chain. This finding not only theoretically aids in exploring the green transmission effect of ESG in the supply chain but also has significant practical implications for improving the ESG performance of enterprises.

The arrangement of this article’s sections is as follows: The first part is the introduction; the second part is the literature review; the third part is the institutional context and theoretical analysis; the fourth part is the research design; the fifth part is the empirical results; the sixth part is the further analysis; the seventh part is the heterogeneity analysis; the eighth part is the conclusion.

2. Literature Review

2.1. Performance Evaluation of SOEs in China

SOEs serve a strategically supportive function within China’s national economic framework, aiming not only to be “larger, superior, and more robust” [7,8] but also to assume a leadership position. Consequently, China has consistently prioritized the reform and development of SOEs. The evaluation of SOEs has evolved throughout time, transitioning from physical production evaluation to economic value added (EVA) assessment; shifting emphasis from economic efficiency to high-quality development; and moving from social responsibility to prioritizing ESG performance [9,10,11,12]. These modifications exemplify the attributes of the era and underscore the progressively vital strategic support function of SOEs. Prior research has concentrated on the determinants of the performance evaluation system of Chinese SOEs, particularly on social and environmental aspects [13,14,15]. The social dimension encompasses the robustness and scope of local audits [16], the mitigation of the internal pay disparity [17], and technological advancements [18], which can bolster SOEs’ dedication to their social responsibilities. Conversely, the fulfillment of social responsibilities by China’s SOEs arises from the interplay of both internal and external motivating factors [19], with external factors comprising market-driven, government-influenced, and socially encouraged elements. In the environmental dimension, engagement in party organization [14], the privatization of SOEs [20], technical innovation [18], and the digital industry can improve the environmental performance of SOEs [21].

2.2. ESG Performance

By adhering to ESG duties, companies can augment their competitive advantages [22], promote operational efficiency, mitigate financial risks, and, therefore, elevate their corporate value [23]. It additionally fosters the enhancement of overall factor productivity, green patents, economic value added, financial performance, and the advancement of comprehensive upgrading indicators [24]. ESG performance can significantly enhance investment efficiency [25,26], increase the likelihood of foreign investment to expand investment scale [27], alleviate external financing constraints [28,29,30], optimize enterprise capital structure [31], reduce financing costs [32,33,34], and improve commercial credit financing [35]. Regarding how to enhance ESG, existing studies have found that party organization governance [9], R&D activities [36], media attention [37], greening of the tax system [38], environmental protection tax levy, and corporate green technological innovations can enhance the corporate ESG performance [39]. Furthermore, the rapid advancement of digital transformation [40], the expansion of firm size [41], and the governance and synergies provided by co-institutional investors can enhance corporate ESG performance [42,43]. Concurrently, ESG performance exhibits a spillover impact. Companies exhibiting robust ESG performance typically demonstrate a pronounced commitment to social responsibility, environmental consciousness, and enhanced governance structures, hence facilitating the establishment of a favorable reputation in the capital market [44,45]. Strong ESG performance can significantly enhance customer perception and satisfaction, thereby attracting new clients and securing the trust of existing ones [46].

The aforementioned literature demonstrates substantial findings; nonetheless, it still exhibits the following deficiencies: Firstly, the prevailing research predominantly examines the economic ramifications of the ESG performance, with the determinants influencing an enterprise’s ESG performance analyzed primarily through the lenses of party organization governance, research and development initiatives, enterprise scale, prevalent institutional investors, media scrutiny, tax system greening, environmental protection tax imposition, and digital transformation, among others. The variables affecting the ESG performance of supply chain firms governed by state-owned enterprises have received no attention. Secondly, current research has not addressed the heterogeneity and asymmetry of the effects of improving the ESG performance. Current research indicates that while the mechanisms and economic ramifications of ESG enhancement have been addressed, the focus predominantly lies on the economic consequences of ESG. There remains significant potential for investigating the heterogeneity of ESG’s impact, and there is a paucity of studies examining the asymmetry and transmission effects of the ESG performance among enterprises within the supply chain context. Thirdly, current research predominantly emphasizes the reform and high-quality development of SOEs, with limited attention given to the value attributes of SOEs in influencing the ESG performance of other enterprises. Specifically, there is a lack of exploration into the leading effect and heterogeneous characteristics of SOEs in enhancing the ESG performance of supply enterprises in the supply chain through an analysis of the equity structure of these enterprises.

3. Institutional Context and Theoretical Analysis

In 2021, China’s State-owned Assets Supervision and Administration Commission mandated that SOEs, central enterprises, and their holding companies listed on the Stock Exchange of Hong Kong Limited (SEHK) integrate ESG into their core operations to enhance corporate social responsibility and serve as exemplars in the development of the ESG framework. Consequently, SOEs possess the governmental backing to assume a pivotal role in enhancing ESG performance for both upstream and downstream entities within the supply chain. The “2022 China ESG Development and Innovation White Article” indicates that the disclosure rate for A-share listed businesses in China is 29.45%, with state-owned enterprises disclosing at a rate of 46.70% and private enterprises at 19.13%, revealing substantial disparities. Given the convergence and copying of information disclosure among market firms, the ESG performance of SOEs may serve as a model for learning effects. Simultaneously, improving ESG performance is intricate and contingent upon the organization’s internal circumstances and external factors. Typically, a firm’s ESG performance improves with an increase in available resources [47]. Given that SOEs possess significant advantages in enterprise scale, ESG information, resource procurement, integration capabilities, and the capacity to comprehend and implement ESG rules, they are well positioned to assume a leadership role in ESG within supply chain interactions.

Firstly, from the standpoint of commercial financial assistance. Companies with elevated ESG ratings frequently motivate upstream and downstream supply chain entities to enhance their environmental and social responsibility standards via contractual terms or particular cooperative agreements [47]. State-owned supply chain core enterprises may offer more advantageous commercial credit policies, such as lengthening payment cycles or enhancing prepayment options, to compel enterprises in the supply chain to implement requisite advancements in green and sustainable development. Utilizing commercial credit incentives, state-owned supply chain core enterprises can effectively motivate enterprises in the supply chain to enhance their investment in environmental protection technology, fulfill their social responsibilities, and optimize governance, thereby elevating the overall ESG standards of the entire supply chain. And then, firms with elevated ESG ratings typically exhibit superior cash flow management and financial stability [48,49]. As the principal entities in the supply chain, state-owned supply chain core enterprises can adeptly allocate capital resources and efficiently mitigate the financial strain on enterprises in the supply chain through prepayment of money or modification of the billing cycle. This not only bolsters the financial stability of enterprises in the supply chain but also augments their investment capability in advancing ESG initiatives. By perpetually enhancing the cash flow of enterprises in the supply chain, state-owned supply chain core enterprises might significantly elevate the ESG standards of all enterprises within the supply chain. Companies exhibiting superior ESG performance typically garner more policy backing and market preference [50], hence establishing a robust basis for state-owned supply chain core enterprises to assume a leadership position within the supply chain. Governments and regulators typically provide preferential policies, including tax incentives, subsidies, and advantageous privileges for green financing, to enterprises demonstrating superior ESG performance. The aforementioned policy support bolsters the capital strength and financial flexibility of state-owned supply chain core enterprises, hence augmenting their capacity to offer substantial commercial credit assistance to enterprises in the supply chain. In a policy- and market-driven context, state-owned supply chain core enterprises can secure adequate resources and favorable incentives to enhance the ESG performance of enterprises in the supply chain.

Secondly, from the standpoint of green technology spillover and awareness dissemination. A robust supply chain partnership is formed between suppliers and customer firms through the execution of formal contracts, establishing a stable trading environment, minimizing cost expenditures during transactions, and enhancing information interoperability and sharing between the parties [51], thereby fostering knowledge spillover effects. The green innovation capacity of state-owned supply chain core enterprises is markedly enhanced by their substantial ESG investment. As trading activities among supply enterprises in the supply chain intensify, corporate executives and technicians facilitate the transfer and diffusion of knowledge and technology through formal or informal exchanges [52], thereby positively influencing the green innovation of enterprises in the supply chain. Moreover, current research indicates that green innovation can enhance firms’ desire to invest in ESG, hence advancing the overall enhancement of the ESG performance levels within the chain. And then, in the supply chain management, the ESG benefits of state-owned supply chain core enterprises can mitigate information asymmetry within the supply chain. Through the consistent publication of ESG reports, state-owned supply chain core enterprises effectively showcase their performance and future strategies in environmental, social, and governance domains, thereby augmenting public information openness. This conduct explicitly conveys to both upstream and downstream entities in the supply chain their authentic green development objectives, environmental criteria, and long-term vision for sustainability. The enhanced transparency of information and the establishment of a trusting relationship alleviate the concerns of enterprises in the supply chain regarding the risks associated with green investment, hence elevating the significance they place on environmental protection and expediting the process of green transformation. While enhancing their own ESG performance, state-owned supply chain core enterprises will communicate explicit environmental and social responsibility directives to upstream and downstream entities along the supply chain [53]. These recommendations delineate explicit requirements for the environmental performance of both upstream and downstream enterprises while also articulating expectations for their social responsibility and governance processes. When state-owned supply chain core enterprises prioritize the enhancement of their ESG performance and demonstrate commitment, upstream and downstream entities will receive significant signals and expectations from these enterprises. This incentive will encourage upstream and downstream enterprises to enhance their environmental consciousness, prioritize environmental concerns, and embrace green innovation strategies [54], hence improving the ESG performance.

Thirdly, from the standpoint of the convergence impact on stakeholders. The “convergence effect” denotes the inclination of companies to uphold behavioral consistency with external organizations due to external constraints within analogous environmental circumstances. The presence of state-owned supply chain core enterprises within the same supply chain network as enterprises in the supply chain fosters a propensity for strategic convergence among the supply chain entities. The economic results of enterprises influence not only enterprises themselves but also numerous stakeholders, thereby significantly encouraging these stakeholders to embrace elevated standards of corporate social responsibility measures [55]. Consequently, when state-owned supply chain core enterprises enhance their ESG performance, affiliated firms adjust their ESG strategy correspondingly. Secondly, state-owned supply chain core enterprises will gain substantial expertise in the development and use of green technologies while enhancing their ESG performance. These green technologies and management techniques not only advance the sustainable growth of state-owned supply chain core enterprises but also serve as exemplary models for other entities within the supply chain to adopt and replicate [56]. State-owned supply chain core enterprises can disseminate their ESG management practices and innovations to enterprises in the supply chain through training, collaborative research and development, and technology transfer. These collaborative forms not only augment the technical and management competencies of the enterprises in the supply chain but also raise their capacity to elevate their ESG performance [57]. To uphold their environmentally conscious brand image, state-owned supply chain core enterprises typically integrate environmental and social responsibility criteria into contracts and cooperation agreements with chain firms [58]. They also offer enhanced business collaboration opportunities and preferential policies to those chain firms that comply with green standards, while communicating elevated expectations for ESG practices to upstream and downstream entities [45]. Consequently, to satisfy the demands of state-owned supply chain core enterprises, sustain relevant business relationships, and retain a competitive edge in the market, enterprises in the supply chain generally respond favorably to these expectations and strive to enhance their ESG performance [59]. This method facilitates the effective transfer of the advantages and managerial expertise of state-owned supply chain core enterprises to other entities within the supply chain, hence enhancing their advancement and development in environmental, social, and governance (ESG) performance.

Based on the above analysis, as shown in Figure 1,this research anticipates that SOEs can assume a pivotal role in improving the ESG performance of entities within the supply chain. This study posits the subsequent hypothesis based on the preceding analysis:

Hypothesis 1.

The enhancement of ESG performance in state-owned supply chain core enterprises positively influences the ESG performance of enterprises in the supply chain, indicating a considerable positive association between the two.

4. Research Design

4.1. Sample Selection

In this article, we select all 16,867 state-controlled listed companies as the initial research samples from 2009 to 2023. The data processing procedure is as follows: Firstly, we exclusively selected sample data that explicitly identifies the names of upstream and downstream enterprises within the supply chain. Observations corresponding to listed companies lacking identified suppliers or customers were subsequently excluded from the dataset, and we deleted the corresponding observations from the dataset, resulting in a total of 5507 observations. Secondly, considering the potential overlap among supply chains, where an enterprise may simultaneously participate in the supply chains of multiple chain-owning enterprises, we defined and filtered state-owned supply chain core enterprises based on the supply chains relevant to the primary business of the enterprises within the chain to mitigate the effects of such overlap and obtained a total of 1400 pieces of data. Finally, by adhering to the aforementioned criteria, we delineated the pairwise relationship between “state-owned supply chain core enterprises” and “enterprises in supply chain”, ultimately yielding 1174 firm-year observations. To mitigate the potential influence of outliers on the regression analysis outcomes, we adjusted all continuous variables by 1% in both directions. The ESG score statistics were sourced from the Hua Zheng rating database, whereas all other financial metrics were derived from the CSMAR database (https://data-csmar-com-443.webvpn.cueb.edu.cn/, accessed on 24 March 2025).

4.2. Variable Definitions

4.2.1. Independent Variable

The independent variable in this study is the ESG performance of state-owned supply chain core enterprises (ESG_soe). The majority of current research uses the ratings provided by third-party agencies as a measure of the ESG performance [22,24,28,30]. The current total of worldwide ESG rating agencies has surpassed 600, with the three most prominent international rating standards being MSCI, KLD, and Sustainalytics. In China, prominent ESG rating agencies comprise Wind, Shangdao Ronglv, Hua Zheng, Harvest Fund, and the International Research Institute of Green Finance at the Central University of Finance and Economics (CUFE), among others. The Hua Zheng ESG rating system is the most widely recognized. The Hua Zheng ESG rating system possesses benefits in evaluation scope, speed, and data capability, rendering it an effective instrument in the investment decision-making analysis process.

Variations in the social environment domestically and internationally, disparities in technical advancement, and differences in the maturity of the ESG rating system can lead to substantial discrepancies in the evaluation outcomes of diverse ESG rating organizations. The Hua Zheng ESG rating system has superior update frequency, extensive coverage, and data dependability, and is more suitably tailored to the unique conditions of the Chinese market [23,26]. The system comprises an abundance of data sources, including publicly available information from listed enterprises, social responsibility reports, and sustainability reports. Furthermore, Hua Zheng ESG incorporates curated data by utilizing technical methods, including machine learning and text mining algorithms, to extract and analyze information from government and regulatory websites, as well as news media sources, thereby constructing a standardized ESG database. This evaluation system aligns the rating data published by Hua Zheng ESG on publicly traded enterprises more closely with the Chinese market, encompassing a broad scope and exhibiting high timeliness.

This article elects to utilize the Hua Zheng ESG ratings to evaluate the ESG performance of state-owned supply chain core enterprises [9,27]. This assessment system divides the ESG performance of enterprises into nine grades from high to low: AAA, AA, A, BBB, BB, B, CCC, CC, C. To facilitate the operationalization of performing regression analysis, drawing on the methodology [60], this article transforms the above ESG rating division into quantitative data. The specific approach is as follows: the assignment method is used to assign scores to the ESG ratings of enterprises, which are sequentially assigned from 9 to 1, with higher scores representing better ESG performance of enterprises. When the Hua Zheng ESG rating is AAA, it is assigned a value of 9; when the ESG rating is C, it is assigned a value of 1, and so on, forming continuous numerical variables for quantitative analysis. Since the data provided by Hua Zheng ESG rating are in quarterly form, this article derives the annual rating results by taking the average.

4.2.2. Dependent Variable

The dependent variable in this article is the ESG performance of enterprises in the supply chain, namely, the ESG performance of both upstream and downstream enterprises. This article utilizes Hua Zheng ESG ratings to evaluate the ESG performance of enterprises in the supply chain in accordance with the measurement of the explanatory variables. To preserve the consistency of the research methodology, the ESG rating data of the enterprises in supply chain are quantitatively processed in the same manner as the independent variables: a value of 1 is assigned to the C-rating, 2 to the CCC-rating, and so forth, culminating in a value of 9 for the AAA-rating, thereby converting it into a continuous variable for subsequent empirical analyses. This method improves data comparability and adheres to operational standards aligned with current studies on ESG rating quantification [60].

4.2.3. Control Variables

This article, as referenced by previous research, primarily includes control variables related to the corporate characteristics of state-owned supply chain core enterprises [61], the corporate governance variables of state-owned supply chain core enterprises, and the corporate characteristics of chain enterprises. The corporate attributes of state-owned supply chain core enterprises encompass return on total assets (Roa_soe), enterprise value (Jzbs_soe), enterprise book-to-market ratio (Mb_soe), and enterprise price-to-earnings ratio (Pe1_soe). The corporate governance variable for state-owned supply chain core enterprises is the enterprise’s dual role (Dual_soe). The corporate attributes of enterprises in supply chain encompass return on total assets (Roa_sc), growth potential (Grow_sc), and relative value (Tobinqa_sc).

Specifically, the return on total assets (Roa_soe) of state-owned supply chain core enterprises is measured by the ratio of SOEs’ year-end net profit to total assets [56]; the enterprise value (Jzbs_soe) of state-owned supply chain core enterprises is measured by the ratio of SOEs’ total market capitalization at year-end to the current year’s earnings before interest, taxes, depreciation, and amortization [62]; the state-owned supply chain core enterprises’ s book-to-market ratio (Mb_soe) is measured using the ratio of total assets to market capitalization of SOEs at the end of the year [61]; the price-to-earnings ratio (Pe1_soe) of state-owned supply chain core enterprises is measured using the ratio of the SOE’s year-end closing price current value to its earnings per share (EPS) where the EPS is the ratio of the current value of the net profit to the current ending value of the paid-in capital; the state-owned supply chain core enterprises’ dual (Dual_soe) is measured using a dummy variable [63], which is 1 if the year-end chairman and general manager of the SOEs is 1, and 0 otherwise [61]; the return on total assets (Roa_sc) of the enterprises in the supply chain is measured using the ratio of the year-end net profit of the enterprises in the supply chain to the total assets [56]; the development capacity of the enterprises in the supply chain (Grow_sc) is measured using the growth rate of year-end net profit of enterprises in the supply chain [61]; and the relative value of enterprises in the supply chain (Tobinqa_sc) is measured using the Tobin’s Q value of enterprises in the supply chain [64]. Fixed effects for industry (Industry) and year (Year) are also accounted for.

This document presents the primary factors in

Table 1. Variable definitions.

Variable Type	Variable Symbol	Variable Description	Calculation Method
Independent variable	ESG_soe	ESG Performance of state-owned supply chain core enterprises	CSI ESG Rating
Dependent variable	ESG_sc	ESG performance of enterprises in the supply chain	CSI ESG Rating
Control variable	Roa_sc	Return on total assets of enterprises in the supply chain	Measured using the ratio of year-end net profit to total assets of the enterprises in the supply chain
	Grow_sc	Capacity development of enterprises in the supply chain	Measured using the growth rate of the year-end net profit of the enterprises in the supply chain
	Tobinqa_sc	Relative value of enterprises in the supply chain	Measured using the Tobin’s Q of the enterprises in the supply chain
	Roa_soe	Return on total assets of state-owned supply chain core enterprises	Measured using the ratio of the year-end net profit to total assets of state-owned supply chain core enterprises
	Jzbs_soe	State-owned supply chain core enterprises’ value multiples	Measured using the ratio of state-owned supply chain core enterprises’ year-end total market capitalization to current year EBITDA
	Mb_soe	Book-to-market ratio of state-owned supply chain core enterprises	Measured using the ratio of year-end total assets to market capitalization of state-owned supply chain core enterprises
	Dual_soe	State-owned supply chain core enterprises with two jobs in one	1 if the state-owned supply chain core enterprises have a combined chairman and general manager at the end of the year, 0 otherwise.
	Pe1_soe	Chain Master SOE P/E Ratio	Measured using the ratio of the current value of the year-end closing price of state-owned supply chain core enterprises to earnings per share, where earnings per share is the ratio of the current value of net income to the current period-end value of paid-up capital

:

4.3. Model Specification

This article employs econometric analysis based on solid theoretical foundations [47,52,53,61] and established research frameworks to empirically examine the potential causal relationship between the ESG performance of state-owned supply chain core enterprises and the ESG performance of enterprises within the chain [54,59,60]. The benchmark model is articulated as follows:

ESG_sc_i,t = α + βESG_soe_i,t + θΣControls + γYear + δIndustry + ε_i,t

(1)

In model (1), i, t denote the industry and the corresponding year, respectively. The dependent variable ESG_sc represents the ESG performance of enterprises in the supply chain. The primary independent variable ESG_soe denotes the ESG rating scores of state-owned supply chain core enterprises, utilized to evaluate their overall performance in environmental, social, and corporate governance. Furthermore, this article includes a set of control variables for state-owned supply chain core enterprises and enterprises in the supply chain to eliminate the influence of other potential causes. To mitigate the influence of alterations in the industry policy or variations in market supply and demand on corporate ESG performance, this article integrates the fixed effects of the relevant industry of the state-owned supply chain core enterprises (Industry) and the year fixed effects into the model (Year).

5. Empirical Results

5.1. Descriptive Statistics

Table 2. Descriptive statistics.

Variable	Mean	SD	Min	Median	Max
ESG_sc	4.434	1.094	1.00	4.00	7.00
ESG_soe	4.215	1.052	1.00	4.00	7.00
Roa_sc	0.036	0.100	−2.75	0.03	0.44
Grow_sc	−0.295	6.950	−94.77	0.10	59.55
Tobinqa_sc	1.554	0.974	0.69	1.30	13.31
Roa_soe	0.042	0.034	0.00	0.03	0.21
Jzbs_soe	25.027	22.341	4.36	17.44	180.89
Mb_soe	0.717	0.251	0.13	0.72	1.42
Dual_soe	1.930	0.255	1.00	2.00	2.00
Pe1_soe	92.230	262.444	3.46	31.74	4161.10

illustrates the following: The average ESG_sc value of enterprises in the chain is 4.434, suggesting that their ESG composite scores are predominantly in the mid to upper range, with most enterprises prioritizing corporate ESG responsibility and allocating resources towards environmental protection and social welfare. The maximum and minimum values are 7.00 and 1.00, respectively, and the standard deviation (1.094) is less than the mean (4.434), indicating that there are certain variations in variables among different enterprises.

Consequently, this article investigates the factors influencing the enhancement of ESG performance within the chain, which holds practical significance. The mean and median of state-owned supply chain core enterprises are 4.215 and 4.00, respectively, with a standard deviation of 1.052. This suggests that the ESG performance of the majority of state-owned supply chain core enterprises is relatively stable, and the variance in the ESG performance among enterprises is minimal. However, the minimum value of 1.00 indicates that the ESG performance of certain SOEs exhibits greater volatility. The statistical outcomes of other variables fall within a tolerable range and are largely congruent with the findings of the current literature. The aforementioned results suggest that the variable selection is predominantly sound.

5.2. Correlation Analysis

Table 3. Correlation analysis.

	ESG_sc	ESG_soe	Roa_sc	Grow_sc	Tobinqa_sc	Roa_soe	Jzbs_soe	Mb_soe	Dual_soe	Pe1_soe
ESG_sc	1.000	0.104 ***	0.167 ***	0.026	0.122 ***	0.034	−0.003	0.042	−0.006	−0.035
ESG_soe	0.094 ***	1.000	−0.053 *	−0.009	0.125 ***	0.164 ***	0.134 ***	0.095 ***	−0.045	0.198 ***
Roa_sc	0.105 ***	−0.028	1.000	0.390 ***	0.319 ***	0.080 ***	−0.019	−0.037	0.034	−0.047
Grow_sc	0.039	0.003	0.142 ***	1.000	0.127 ***	0.046	0.010	−0.046	0.005	0.003
Tobinqa_sc	0.142 ***	−0.057 *	0.076 ***	−0.065 **	1.000	0.096 ***	0.195 ***	0.358 ***	−0.005	0.174 ***
Roa_soe	0.018	0.155 ***	0.062 **	0.058 **	0.042	1.000	0.463 ***	0.176 ***	0.012	0.627 ***
Jzbs_soe	0.021	0.161 ***	−0.036	−0.012	0.053 *	0.325 ***	1.000	0.399 ***	0.006	0.790 ***
Mb_soe	0.043	0.097 ***	0.012	0.001	0.191 ***	0.222 ***	0.304 ***	1.000	−0.042	0.123 ***
Dual_soe	−0.007	−0.036	0.016	0.001	−0.004	0.007	0.010	−0.031	1.000	−0.011
Pe1_soe	−0.047	0.114 ***	−0.074 **	−0.024	−0.036	0.274 ***	0.138 ***	0.159 ***	0.004	1.000

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively.

presents the correlation coefficients among the primary variables discussed in the study. Table 3 reveals that the correlation coefficients between the ESG performance of state-owned supply chain core enterprises, denoted as ESG_soe, and the ESG performance of enterprises in the supply chain, referred to as ESG_sc, are both positive and statistically significant at the 1% level. This indicates that an enhancement in the ESG performance of state-owned supply chain core enterprises correlates with a substantial increase in the ESG performance of enterprises in the supply chain, thereby affirming the hypothesis posited in this article.

An examination of the correlation coefficients reveals that neither the Pearson nor the Spearman correlation coefficient surpasses 0.5, indicating a weak correlation among the variables. Consequently, it can be tentatively concluded that the regression model comprising these independent variables is likely to mitigate the influence of multicollinearity on the regression outcomes. The correlation among individual variables is not significant using the significance level of the correlation coefficients. The Pearson and Spearman correlation coefficients just indicate the association between two variables without accounting for the influence of additional variables. Consequently, it is essential to further regulate the impact of additional factors by regression analysis to obtain more trustworthy conclusions.

5.3. Multivariate Regression Analysis

This study employs model (1) to empirically evaluate the fundamental hypotheses outlined in the theoretical analysis section. Column (1) in

Table 4. Multivariate regression analysis.

	(1)	(2)
	ESG_sc	ESG_sc
ESG_soe	0.093 ***	0.076 **
	(3.037)	(2.511)
Roa_sc	−0.144 ***	−0.154 ***
	(−4.370)	(−4.629)
Grow_sc	1.199 ***	1.022 ***
	(3.206)	(2.749)
Tobinqa_sc	−0.005	−0.003
	(−0.941)	(−0.571)
Roa_soe	1.500	1.082
	(1.347)	(0.978)
Jzbs_soe	0.007 ***	0.004 **
	(3.407)	(2.058)
Mb_soe	0.349 **	0.037
	(2.080)	(0.210)
Dual_soe	−0.023	−0.012
	(−0.184)	(−0.101)
Pe1_soe	−0.000 **	−0.000 *
	(−2.551)	(−1.797)
Year		0.035 ***
		(4.664)
Industry		0.003
		(1.630)
Cons	3.806 ***	−66.927 ***
	(11.260)	(−4.417)
N	1174	1174
Adj. R2	0.041	0.061

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

examines solely the ESG leading effect of state-owned supply chain core enterprises without accounting for the influences of industry and year, whereas column (2) further controls for these characteristics. The estimation results indicate that the enhancement of ESG ratings among state-owned supply chain core enterprises can substantially influence the ESG performance of enterprises in the supply chain. This corroborates the hypothesis of this article.

In addition, the coefficient of return on total assets (Roa_sc) of enterprises in the supply chain is significantly negative at the 1% level. It indicates that the return on total assets (ROA) of enterprises in the supply chain is significantly negatively correlated with ESG performance. This may be due to the fact that high ROA enterprises focus more on short-term financial performance, thus reducing long-term investment in ESG areas. Meanwhile, ESG investment itself may increase costs or reduce operational efficiency in the short term, which, in turn, negatively affects financial performance.

The growth of enterprises in the supply chain (Grow_sc) is significantly positive at the 1% level, indicating a significant positive association between the growth of enterprises in the supply chain and their ESG performance. High-growth enterprises usually have stronger resource allocation capabilities and technological strength, and are able to invest more resources in ESG-related areas (e.g., digital emission reduction technologies, green production processes, etc.), thus systematically improving their ESG performance.

The regression coefficient of the value multiple of SOEs (Jzbs_soe) is significantly positive at the 1% level, indicating that the increase in the value multiple of SOEs can significantly improve the ESG performance of enterprises in the supply chain. A high value multiple of an enterprise reflects the capital market’s optimistic expectation of the enterprise’s future growth potential, indicating that investors recognize the long-term value of its business model, industry position, or strategic layout. SOEs with high value multiples are usually considered to have a stronger supply chain influence, improving the ESG performance of enterprises in the supply chain.

The regression coefficient of the price-to-earnings ratio of SOEs (Pe1_soe) shows a stable negative relationship although the value is extremely small, indicating that the price-to-earnings ratio of SOEs has a weak inhibitory effect on the ESG performance of enterprises in the supply chain, which may reflect the short-term profitability pressure that causes SOEs to neglect supply chain ESG regulation.

The SOE book-to-market ratio (Mb_soe) is significantly positive in column (1), indicating that SOEs with higher book-to-market ratios tend to have better ESG performance than other enterprises in the supply chain. This coefficient remains positive but insignificant after adding year and industry control variables, which may be due to the fact that the contribution of book-to-market ratio to the ESG of enterprises in the supply chain is explained by the time trend and industry characteristics.

The coefficients of Tobin’s Q of enterprises in the supply chain (Tobinqa_sc), return on total assets of SOEs (Roa_soe), and the situation of two positions in SOEs (Dual_soe) are insignificant, indicating that the capital market valuation of enterprises in the supply chain, the short-term financial performance of SOEs, and the centralization of decision-making power of SOEs are not the key factors influencing the ESG of enterprises in the supply chain. The results of the coefficients of all the above control variables are consistent with the existing literature.

5.4. Robustness Tests

5.4.1. Replacement Variables

China’s ESG rating system exhibits diversity, with notable discrepancies in the weight distribution of sub-dimensions across various rating agencies. To mitigate the potential confounding issue of weight distribution in the ESG evaluation index system, this article utilizes the corporate ESG scoring system from the China Research Data Service Platform (CNRDS) for assessment and reconstructs the core explanatory variables (CnrdsESG_soe) and the explained variables (CnrdsESG_sc), ensuring consistency in the sample timeframe. Robustness tests were conducted, and the results are presented in columns (1) and (2) of

Table 5. Replacement variables and sample capacity.

	(1)	(2)	(3)	(4)
	CnrdsESG_sc	CnrdsESG_sc	ESG_sc	ESG_sc
CnrdsESG_soe	0.167 ***	0.071 **
	(6.142)	(2.461)
ESG_soe			0.100 ***	0.084 ***
			(3.276)	(2.752)
Roa_sc	7.462 **	5.623 *	1.231 ***	1.054 ***
	(2.347)	(1.831)	(3.296)	(2.839)
Grow_sc	−0.099 **	−0.072	−0.005	−0.003
	(−2.171)	(−1.637)	(−0.952)	(−0.583)
Tobinqa_sc	−0.577 **	−0.511 *	−0.139 ***	−0.150 ***
	(−2.061)	(−1.865)	(−4.211)	(−4.510)
Roa_soe	−8.271	−16.198 *	1.324	0.915
	(−0.881)	(−1.785)	(1.189)	(0.828)
Jzbs_soe	0.012	−0.027	0.007 ***	0.005 **
	(0.672)	(−1.515)	(3.433)	(2.086)
Mb_soe	5.161 ***	0.619	0.342 **	0.034
	(3.574)	(0.421)	(2.036)	(0.193)
Dual_soe	0.375	0.468	−0.022	−0.012
	(0.359)	(0.465)	(−0.176)	(−0.098)
Pe1_soe	−0.000	0.001	−0.000 **	−0.000 *
	(−0.130)	(1.040)	(−2.557)	(−1.805)
Year		0.643 ***		0.035 ***
		(9.686)		(4.591)
Industry		−0.023		0.003 *
		(−1.427)		(1.760)
Cons	18.392 ***	−1269.859 ***	3.775 ***	−65.814 ***
	(6.726)	(−9.547)	(11.186)	(−4.346)
N	1174	1174	1169	1169
Adj. R2	0.070	0.138	0.042	0.061

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

. All aforementioned estimation results indicate that the benchmark outcomes are robust.

5.4.2. Replacement of Sample Capacity

Financial indicators are crucial for evaluating the financial health and operational efficacy of an organization. Variations in the market environment, business model, and competitive landscape across different industries may result in discrepancies in their financial statistics. The financial metrics of the agriculture, forestry, animal husbandry, and fishing sectors differ significantly from those of other industries. To mitigate the potential impact of these discrepancies on the ESG performance of enterprises in the supply chain, this study excludes samples from the agriculture, forestry, animal husbandry, and fishery sectors to re-evaluate the baseline regression equations, with the results presented in columns (3) and (4) of Table 5. The coefficients for the ESG performance of enterprises in the supply chain, ESG_sc, are significantly positive, irrespective of industry and year controls, hence reaffirming the validity of prior findings.

5.4.3. Adding Control Variables

To systematically validate the robustness of this article’s conclusions, additional control factors pertinent to the Chinese capital market and business environment of the firms are incorporated, based on the original control variables. This article utilizes the financing constraints encountered by enterprises in the supply chain (SA_sc) [65], the tax burden on enterprises in the supply chain (Tax_sc) [66], and the stock liquidity in the capital market of SOEs (ILLIQ_soe) to assess this influential factor [67]. The stock liquidity indicator is formulated as follows:

$$ILLI{Q}_{i,y}={10}^8\ast 1/D_{i,y}{{\displaystyle \sum }}_{t=1}^{D_{iy}}\left|R_{iyd}\right|/VOL{D}_{ivyd}$$

(2)

D_iy denotes the quantity of effective trading days for stock i in cycle y; R_iyd signifies the daily return of stock i on day d of cycle y, accounting for the reinvestment of cash dividends; and VOLD_ivyd indicates the transaction volume of stock i on day d of cycle y. The regression outcomes subsequent to the inclusion of control variables are presented in the

Table 6. Addition of control variables.

	ESG_sc
ESG_soe	0.057 *
	(1.895)
Roa_sc	1.210 ***
	(3.298)
Grow_sc	−0.005
	(−0.938)
Tobinqa_sc	−0.117 ***
	(−3.571)
Roa_soe	1.189
	(1.101)
Jzbs_soe	0.004 **
	(2.104)
Mb_soe	0.067
	(0.382)
Dual_soe	−0.022
	(−0.183)
Pe1_soe	−0.000
	(−1.349)
SA_sc	0.869 ***
	(7.498)
Tax_sc	−0.943 **
	(−2.000)
ILLIQ_soe	0.462 *
	(1.856)
Year	0.058 ***
	(7.211)
Industry	0.005 **
	(2.452)
Cons	−109.037 ***
	(−6.855)
N	1174
Adj. R2	0.105

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

, and the ensuing results demonstrate that the ESG performance of state-owned supply chain core enterprises continues to hold a predominant position.

5.4.4. Replacement of Regression Models

Existing studies on the factors affecting corporate ESG performance mainly use an OLS regression model [68,69], an Ologit multiple ordered regression model, for empirical testing [38]. There are also some research articles on policy that use DID models [70,71]. Considering that the Hua Zheng ESG ratings used in this article take the values of 1–9 ordered categorical variables, in order to verify the validity of the regression results of the OLS model, this article refers to the existing studies and replaces the benchmark model with the ordered model Ologit for the robustness test. The results are shown in the

Table 7. Replacement of regression models.

	ESG_sc
ESG_soe	0.151 ***
	(2.893)
Roa_sc	3.542 ***
	(3.388)
Grow_sc	−0.007
	(−0.823)
Tobinqa_sc	−0.279 ***
	(−4.861)
Roa_soe	1.846
	(0.960)
Jzbs_soe	0.009 **
	(2.358)
Mb_soe	0.003
	(0.009)
Dual_soe	−0.033
	(−0.160)
Pe1_soe	−0.000 *
	(−1.826)
Year	0.067 ***
	(4.981)
Industry	0.005
	(1.525)
cut1	130.648 ***
	(4.855)
cut2	132.117 ***
	(4.910)
cut3	133.703 ***
	(4.969)
cut4	135.407 ***
	(5.031)
cut5	137.254 ***
	(5.098)
cut6	139.233 ***
	(5.170)
N	1174
$r^2$_p	0.0265

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

, and the regression results after replacing the estimated model are still significant.

5.4.5. Normalization

To further validate the robustness of the benchmark regression results, this article normalizes the dependent and independent variables, together with all control variables, for all samples as follows:

$$X^{\prime }={\displaystyle \frac{X-X_{min}}{X_{max}-X_{min}}}$$

(3)

The regression is conducted again following normalization using the specified algorithm. The exact results are presented in the

Table 8. Normalization.

	ESG_sc’
ESG_soe’	0.076 **
	(2.511)
Roa_sc’	0.543 ***
	(2.749)
Grow_sc’	−0.078
	(−0.571)
Tobinqa_sc’	−0.323 ***
	(−4.629)
Roa_soe’	0.037
	(0.978)
Jzbs_soe’	0.132 **
	(2.058)
Mb_soe’	0.008
	(0.210)
Dual_soe’	−0.002
	(−0.101)
Pe1_soe’	−0.179 *
	(−1.797)
Year	0.006 ***
	(4.664)
Industry	0.001
	(1.630)
Cons	−11.744 ***
	(−4.656)
N	1174
Adj. R2	0.061

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

, and the subsequent findings demonstrate the robustness of this article’s conclusions.

5.5. Endogeneity Test

5.5.1. Instrumental Variables

Endogeneity issues, including “mutual causation” and omitted variables, may exist between the ESG performance of state-owned supply chain core enterprises and that of the enterprises in the supply chain. In light of the aforementioned issues, this study formulates an instrumental variable for the ESG rating performance of state-owned supply chain core enterprises, drawing on the methodology [27]. This study selects the number of companies invested in by “Fan-ESG” funds (IV-esg1) and the market value of investment (IV-esg2) as instrumental variables for the ESG of chain-owning SOEs. Initially, public funds concentrating on ESG issues, as shareholders, possess the obligation and authority to oversee and advance the improvement of ESG (environmental, social, and governance) performance within their portfolio corporations. When companies do not satisfy the ESG standards of public funds, these funds are entitled to apply pressure on the companies to enhance their ESG performance through mechanisms like as voting rights, indicating a correlation between this instrumental variable and the ESG status of state-owned supply chain core enterprises. And then, ESG-themed public funds are typically overseen by professional fund management entities or investment teams, and their investment decision-making processes are autonomous from other enterprises and market influences, thereby fulfilling the exogeneity criterion. Moreover, the market value of chain-owning SOEs funded by “Fan-ESG” investments is not directly correlated with the ESG assessments of other entities within the chain, hence adhering to the idea of exclusivity. The two-stage least squares (2SLS) method of instrumental variables indicates that the number of companies invested in by “Fan-ESG” funds (IV-esg1) and the market capitalization of the investment (IV-esg2) significantly enhance the ESG performance of the enterprises within the chain. As shown in

Table 9. Instrumental variables.

	(1)	(2)
	IV-esg1	IV-esg2
ESG_soe	0.417 **	0.362 ***
	(2.332)	(2.794)
Roa_sc	4.487 ***	4.389 ***
	(6.188)	(6.305)
Grow_sc	−0.008	−0.008
	(−1.233)	(−1.212)
Tobinqa_sc	−0.170 ***	−0.174 ***
	(−4.424)	(−4.689)
Roa_soe	−0.086	0.147
	(−0.058)	(0.105)
Jzbs_soe	0.006 **	0.006 **
	(2.275)	(2.273)
Mb_soe	0.018	0.028
	(0.089)	(0.140)
Dual_soe	0.012	0.006
	(0.088)	(0.045)
Pe1_soe	−0.000	−0.000
	(−1.229)	(−1.334)
Year	0.022 **	0.024 ***
	(2.386)	(2.609)
Industry	0.003	0.003
	(1.481)	(1.506)
Cons	−42.722 **	−44.723 **
	(−2.300)	(−2.493)
N	1043	1043
Adj. R2	−0.009	0.018

Note: **, and *** represent the significance level of 5%, and 1%, respectively; the values of t are in parentheses, respectively.

,The findings of pertinent statistical tests indicate that the hypothesis asserting that the aforementioned instrumental factors are weak can be rejected. Even after accounting for endogenous impacts, the ESG leadership of state-owned supply chain core enterprises can still substantially enhance the ESG ratings of affiliated firms.

5.5.2. PSM

This research employs the propensity score matching (PSM) method to limit the influence of varying characteristics of state-owned listed companies with differing ESG levels on the regression outcomes. The process begins by calculating the annual average ESG performance of each sample state-owned supply chain core enterprise and categorizing the firms into two groups based on the 66th percentile, designating those with superior ESG levels as the experimental group and those with inferior levels as the control group. Subsequently, the control variables in the benchmark regression are designated as covariates, and near-neighbor kernel matching is performed based on propensity score values. Figure 2 displays the two groups of matched samples, which adhere to the common support assumption and exhibit no significant variation in the means of the covariates, thus passing the smoothness test.

Table 10. PSM test results.

	ESG_sc
ESG_soe	0.121 *
	(0.072)
N	1174

Note: * represent the significance level of 10%.

presents the regression results post-matching, revealing that the regression coefficient for the ESG level of state-owned enterprises is 0.121, which is significantly positive at the 10% level. This indicates that the ESG performance of state-owned supply chain core enterprises can improve the ESG performance of enterprises in the supply chain, and the benchmark regression results are robust.

6. Further Analysis

6.1. Asymmetry of Ownership Nature of Core Enterprises and Enterprises in the Supply Chain

6.1.1. The Effect of the Nature of the Equity of the Supply Chain Core Enterprise

To comprehensively understand the interactions between supply chain core enterprises and enterprises in the supply chain regarding their ESG performance and the challenges encountered, it is essential to consider the ownership structure of both entities. SOEs exhibit more significant disparities in ESG performance compared to private enterprises [72]. In the context of China’s long-term objectives of achieving “peak carbon and carbon neutrality”, SOEs possess a significant impetus to expedite the green transformation of both upstream and downstream enterprises due to the necessity of fulfilling major national strategic tasks, adhering to environmental protection performance assessment standards, and augmenting the core competitiveness of state-owned capital. This study posits that the influence of the supply chain core enterprise on the enterprises inside the chain will be substantial only when the supply chain core enterprise is state-owned.

This article substitutes the primary explanatory variables with ESG_private for the non-SOE sample and re-evaluates the baseline regression equation. The regression results presented in column (1) of

Table 11. Asymmetry of ownership nature of core enterprises and enterprises in the supply chain.

	(1)	(2)	(3)
	ESG_sc	Supply Chain Core Enterprises Are SOEs	Enterprises in the Chain Are Non-SOEs
ESG_private	0.044
	(1.586)
ESG_soe		0.041	0.206 ***
		(1.148)	(3.529)
Roa_sc	3.744 ***	0.230	4.763 ***
	(7.980)	(0.598)	(4.297)
Grow_sc	−0.007 **	0.002	−0.026
	(−2.328)	(0.360)	(−1.219)
Tobinqa_sc	−0.116 ***	−0.110 **	−0.160 ***
	(−5.103)	(−2.357)	(−3.056)
Roa_soe	0.691	1.127	0.542
	(0.843)	(0.918)	(0.219)
Jzbs_soe	0.000	0.007 ***	0.000
	(0.389)	(3.034)	(0.025)
Mb_soe	0.178	−0.060	0.478
	(1.310)	(−0.300)	(1.200)
Dual_soe	0.131 **	−0.093	0.269
	(2.463)	(−0.614)	(1.209)
Pe1_x	0.000	−0.000 ***	0.000
	(0.912)	(−2.869)	(0.250)
Year	0.020 ***	0.031 ***	0.035 **
	(3.527)	(3.286)	(2.219)
Industry	0.005 ***	0.007 ***	−0.001
	(2.923)	(3.180)	(−0.390)
Cons	−36.096 ***	−57.523 ***	−67.745 **
	(−3.187)	(−3.075)	(−2.143)
N	1889	781	344
Adj. R2	0.048	0.051	0.149
		Difference	(1) − (2) = −0.165 ** The p-value is 0.010

Note: **, and *** represent the significance level of 5%, and 1%, respectively; the values of t are in parentheses, respectively.

are insignificant for a non-state supply chain core enterprise, despite the regression coefficients being positive. This research indicates that when supply chain core enterprises are privately owned, they may receive greater governmental resource support; yet, these resources do not entirely benefit their upstream and downstream partners. While private supply chain core enterprises may possess greater flexibility and market adaptability compared to state-owned supply chain core enterprises, these advantages do not sufficiently incentivize upstream and downstream firms to enhance their ESG performance, thereby diminishing the impact of private supply chain core enterprise ESG improvements on the enterprises in the supply chain. This discovery further substantiates the hypothesis of this article.

6.1.2. Impact of the Nature of the Equity of the Enterprises in the Supply Chain

The characteristics of equity are considered a crucial factor affecting the ESG performance of companies, owing to the variations in their ownership structures and the motivations behind their ESG initiatives. The primary objective of ESG implementation for state-owned enterprises is to adhere to national regulations, meet public expectations, and serve as a model, rather than to seek economic gains. For privately owned firms, the primary incentive for enhancing ESG performance is to get tangible advantages, such as alleviating financial limitations and mitigating company risks. In contrast to non-state enterprises in the supply chain, state-owned enterprises in the supply chain possess a distinct advantage in resource access, tax exemptions, project land, and other areas, which contributes to their reduced reliance on primary enterprises in the supply chain [73]. This research posits that the ESG performance of non-state-owned enterprises in the supply chain will be more significantly affected by state-owned supply chain core enterprises.

This article divides the samples into two groups according to whether the enterprises in the chain are state-owned enterprises. The regression results are shown in columns (2) and (3) in Table 11, which demonstrate that when the enterprises in the chain are non-SOEs, the leading effect of ESG in the state-owned supply chain core enterprises is more significant. This result shows that although non-SOEs may not be as resourceful as SOEs, their decision-making process is more agile and market oriented. When the enterprises in the supply chain are non-SOEs, they tend to rely more on the strength of the SOEs and the market mechanism to enhance their environmental, social, and governance (ESG) performance. At the same time, these non-SOEs in the chain show a high degree of market adaptability and flexibility in the process of improving ESG performance, which will also promote the state-owned supply chain core enterprises in the chain to accelerate the pace of improving the ESG level.

In conclusion, the potential asymmetry in resource distribution and policy endorsement amplifies the influence of ESG practices of chain master enterprises on those of enterprises in the supply chain, particularly when there is a disparity in the equity structure. Specifically, when the supply chain core enterprise is an SOE and the enterprises in the supply chain are non-SOEs, the leading effect of the supply chain core enterprise ESG on the enterprises in the supply chain ESG is more significant.

6.2. Asymmetry in the Supply Chain Positions of Upstream and Downstream Enterprises

The primary influence of ESG performance in state-owned supply chain core enterprises is determined not only by the overarching attributes of the supply chain but also by the enterprises’ positioning within the chain, exhibiting an “asymmetric” nature. The ESG benefits of state-owned supply chain core enterprises can significantly encourage upstream companies to advance in green innovation via the spillover impact of green technology [52]. To uphold their green reputation, state-owned supply chain core enterprises often impose elevated environmental standards and sustainable development criteria on upstream firms, hence incentivizing these enterprises to consistently enhance and innovate in green technology and ESG management. Conversely, for upstream corporations, SOEs occupy a buyer’s position, possessing comparatively greater bargaining strength and, hence, a heightened ability to influence upstream entities. Nonetheless, for downstream enterprises, the impact of state-owned supply chain core enterprises may be quite small. This is due to the heightened competitiveness of the contemporary market, characterized by buyer dominance. Although state-owned enterprises possess a competitive edge in the product market, it is merely relative rather than absolute. This article posits that the influence of state-owned supply chain core enterprises will be more pronounced when the enterprises in the supply chain are situated upstream in the supply chain.

This article categorizes enterprises in the supply chain into upstream firms (ESG_supplier) and downstream firms (ESG_customer) based on their relative position to state-owned supply chain core enterprises in the supply chain, with each subsample examined independently.

Table 12. Asymmetry in the supply chain positions of upstream and downstream enterprises.

	(1)	(2)
	ESG_Customer	ESG_Supplier
ESG_soe	0.069 *	0.095 *
	(1.908)	(1.714)
Roa_sc	−0.220 ***	−0.000
	(−6.200)	(−0.002)
Grow_sc	0.936 **	2.843 **
	(2.318)	(2.582)
Tobinqa_sc	−0.014 *	0.005
	(−1.957)	(0.629)
Roa_soe	0.843	1.736
	(0.685)	(0.739)
Jzbs_soe	0.005 **	0.003
	(1.990)	(0.781)
Mb_soe	0.096	0.031
	(0.465)	(0.088)
Dual_soe	0.063	−0.266
	(0.452)	(−1.125)
Pe1_soe	−0.000 **	0.000
	(−2.282)	(0.071)
Year	0.031 ***	0.038 **
	(3.234)	(2.567)
Industry	0.005 **	−0.001
	(2.399)	(−0.333)
Cons	−58.966 ***	−72.595 **
	(−3.041)	(−2.436)
N	738	436
Adj. R2	0.088	0.041
Difference	(2) − (4) = −0.025 The p-value is 0.337

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

demonstrates that the regression coefficients are considerably positive, irrespective of the enterprises’ relative positions in the upstream or downstream segments of the supply chain. When enterprises in the supply chain are positioned upstream in the supply chain, the regression coefficient is 0.095, exceeding that of downstream enterprises; however, the between-group coefficient difference test reveals no significant disparity between the two groups. The ESG performance of state-owned supply chain core enterprises significantly influences both upstream and downstream entities, with a comparatively higher effect on downstream enterprises.

7. Heterogeneity Analysis

7.1. Size of SOEs

Advancements in science, technology, and innovation necessitate robust infrastructure, sufficient research materials, and sophisticated research equipment. Large SOEs typically possess greater resources and capital, enabling them to allocate more towards research and development, technical advancement, and market expansion. Conversely, small and medium-sized state-owned enterprises exhibit specific shortcomings in this domain relative to major state-owned enterprises [74]. In contrast to conventional SOEs, large SOEs typically possess a heightened sense of social responsibility, a monopolistic position, and a robust capacity to integrate and manage resources, hence providing more substantial support to upstream and downstream firms compared to small and medium-sized SOEs. This article posits that when the chain master enterprise is a larger-scale state-owned supply chain core enterprise, its ESG performance will exert a more pronounced leading influence on the firms within the chain.

This study investigates the heterogeneous impact of the ESG performance of state-owned supply chain core enterprises of varying sizes on the ESG performance of enterprises in the supply chain. The total sample is categorized into two groups—large-scale and small-scale enterprises—according to the median annual firm size within the respective industry of the state-owned supply chain core enterprises. Regression analyses are conducted separately for these two groups using model (1). The regression results in

Table 13. Impact of the size of SOEs.

	(1)	(2)
	Large-Scale	Small-Scale
ESG_soe	0.124 ***	0.027
	(2.678)	(0.590)
Roa_sc	2.322 ***	0.723 *
	(2.704)	(1.702)
Grow_sc	−0.005	−0.002
	(−0.611)	(−0.295)
Tobinqa_sc	−0.206 ***	−0.082 *
	(−4.332)	(−1.740)
Roa_soe	3.362 **	−1.556
	(2.103)	(−0.955)
Jzbs_soe	−0.000	0.005 *
	(−0.035)	(1.749)
Mb_soe	0.239	0.161
	(0.845)	(0.564)
Dual_soe	−0.007	0.054
	(−0.040)	(0.300)
Pe1_soe	−0.000	−0.000
	(−0.249)	(−1.408)
Year	0.008	0.055 ***
	(0.701)	(5.290)
Industry	0.002	0.008 ***
	(0.796)	(2.814)
Cons	−12.926	−107.675 ***
	(−0.549)	(−5.122)
N	574	575
Adj. R2	0.069	0.089
Difference	(1) − (2) = 0.098 * The p-value is 0.065

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

indicate that the coefficients of the explanatory variables in the large-scale group are significantly positive at the 1% level, whereas the coefficients in the small-scale group are positive but lack statistical significance. This discrepancy has been validated by the test for differences in coefficients between groups. This indicates that the influence of the ESG performance of state-owned supply chain core enterprises on the ESG performance of enterprises in the supply chain is more pronounced in large-scale state-owned enterprises than in small-scale state-owned supply chain core enterprises. This outcome can be ascribed to two factors: firstly, the enlargement of enterprise scale can produce economies of scale, allowing large-scale state-owned enterprises to achieve cost reductions and enhance efficiency in production, operations, research and development, innovation, and marketing. Consequently, they are better positioned to undertake substantial R&D investments, conduct extensive market trials, and expedite promotion, thereby accelerating ESG advancements and fostering synergistic effects among supply enterprises in the supply chain. Secondly, due to their extensive business domains, large-scale state-owned enterprises will accumulate greater knowledge and technological expertise over time. This accumulated knowledge and experience will gradually produce a spillover effect, enhancing supply chain synergies and robustly supporting the transmission of improved ESG performance.

7.2. The Number of Years on the Market

The duration of a company’s listing influences its resource accumulation, supply chain stability, and risk resilience [75]. Enterprises with an extended listing period have, after years of development, amassed greater resources in cash, technology, and market channels, thereby achieving a comparatively higher level of autonomy in business development. In supply chain dynamics, more established state-owned supply chain core enterprises will exert greater control on upstream and downstream entities. Upstream and downstream corporations will increasingly focus on fulfilling the demands of SOEs across numerous dimensions, including environmental, social, and governance (ESG) criteria, owing to their constrained resources and reliance on the state-owned supply chain core enterprises. This research posits that the ESG performance of long-listed state-owned supply chain core enterprises will exert a more pronounced influence on upstream and downstream firms.

This research categorizes the sample into two categories according to the median duration of the chain-owning SOE listing to validate this push side. The regressions are performed using model (1), and the results are presented in

Table 14. The impact of the number of years on the market.

	(1)	(2)
	Long Years on the Market	Short Years on the Market
ESG_soe	0.140 ***	0.045
	(3.052)	(1.014)
Roa_sc	4.706 ***	0.246
	(5.447)	(0.569)
Grow_sc	−0.017 **	−0.001
	(−1.976)	(−0.192)
Tobinqa_sc	−0.142 ***	−0.148 ***
	(−3.013)	(−2.985)
Roa_soe	−0.614	2.735
	(−0.397)	(1.555)
Jzbs_soe	0.005	0.004
	(1.254)	(1.546)
Mb_soe	0.381	−0.068
	(1.323)	(−0.263)
Dual_soe	0.096	−0.175
	(0.443)	(−1.083)
Pe1_soe	−0.000	−0.000
	(−1.244)	(−0.913)
Year	0.012	0.048 ***
	(1.025)	(4.502)
Industry	0.004	0.003
	(1.285)	(1.157)
Cons	−21.550	−92.353 ***
	(−0.898)	(−4.305)
N	539	562
Adj. R2	0.093	0.067
Difference	(1) − (2) = 0.096 * The p-value is 0.073

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

. The regression analysis indicates that the influence of state-owned supply chain core enterprises’ ESG performance on the ESG performance of chain businesses is more pronounced in the cohort with extended listing durations, and this finding has successfully undergone the coefficient difference test between groups. This outcome may stem from the observation that state-owned supply chain core enterprises with a shorter listing duration typically exhibit a relative deficiency in market resource accumulation, rendering them somewhat reliant on upstream and downstream entities throughout business expansion. Currently, in response to the ESG impact of state-owned supply chain core enterprises, while upstream and downstream companies will proactively integrate relevant concepts and practices into their business operations to improve brand image and market competitiveness, the impetus for such proactive adaptation is comparatively feeble.

7.3. Market Competition

The level of market rivalry may influence the business conduct of enterprises by impacting their resource allocation. Organizations in a highly competitive landscape encounter increased pressure from rivals, necessitating efforts across all dimensions to develop their competitive advantages [76]. The intense competition in the external market amplifies the market pressure on state-owned supply chain core enterprises, prompting them to share their expertise in enhancing ESG performance to bolster their control and influence. Consequently, the ESG performance of state-owned supply chain core enterprises is more likely to be acknowledged and assimilated by enterprises in the supply chain, thus creating a leading and demonstrative effect. Secondly, owing to the intense market competition, chain-owning firms are inclined to offer increased financial and technological support to enterprises in the supply chain, facilitating resource sharing and value co-creation, hence enhancing the ESG performance of enterprises in the supply chain. This article posits that the impact of the ESG performance of state-owned supply chain core enterprises is more pronounced under conditions of heightened market rivalry.

This article categorizes the total sample into two groups—high-market-competitive industries and low-market-competitive industries—based on the median annual concentration value of the industry associated with state-owned supply chain core enterprises. This is determined by the ratio of the operating revenues of the industry’s top five and top ten firms to the overall industry’s operating revenues. Regression analyses are conducted separately for each group using model (1), with the results presented in

Table 15. Impact of market competition.

	(1)	(2)	(3)	(4)
	High Top 5	Low Top 5	High Top 10	Low Top 10
ESG_soe	0.127 ***	0.018	0.117 ***	0.016
	(2.985)	(0.394)	(2.680)	(0.356)
Roa_sc	4.306 ***	3.641 ***	3.723 ***	3.847 ***
	(3.992)	(4.300)	(3.286)	(4.542)
Grow_sc	0.010	−0.009	0.010	−0.009
	(0.715)	(−1.490)	(0.754)	(−1.493)
Tobinqa_sc	−0.128 ***	−0.276 ***	−0.111 **	−0.183 ***
	(−2.763)	(−4.541)	(−1.996)	(−3.585)
Roa_soe	1.213	0.620	0.071	0.660
	(0.812)	(0.311)	(0.049)	(0.320)
Jzbs_soe	0.006 **	0.004	0.005 **	0.004
	(2.253)	(1.039)	(2.040)	(0.941)
Mb_soe	0.297	−0.006	0.212	0.103
	(1.214)	(−0.021)	(0.870)	(0.347)
Dual_soe	0.089	0.050	−0.003	0.172
	(0.502)	(0.277)	(−0.017)	(0.997)
Pe1_soe	−0.000 *	−0.000	−0.000 *	0.000
	(−1.852)	(−0.407)	(−1.887)	(0.202)
Year	0.019 *	0.043 ***	0.019 *	0.040 ***
	(1.712)	(3.555)	(1.793)	(3.359)
Industry	0.003	0.007 **	0.002	0.006**
	(1.088)	(2.380)	(0.720)	(2.045)
Cons	−34.302	−81.580 ***	−35.072	−77.586 ***
	(−1.564)	(−3.412)	(−1.625)	(−3.232)
N	547	534	550	527
Adj. R2	0.062	0.107	0.041	0.097
Difference	(1) − (2) = 0.109 ** The p-value is 0.045		(1) − (2) = 0.100 * The p-value is 0.062

Note: *, **, and *** represent the significance level of 10%, 5%, and 1%, respectively; the values of t are in parentheses, respectively.

. The regression estimation results indicate that the impact of the ESG performance of state-owned supply chain core enterprises is more pronounced in highly competitive industries, irrespective of the measurement employed, and this disparity has been confirmed through the coefficient difference test between groups. This may stem from the intense competition within the industry, where firms encounter an elevated risk of product substitution and market share erosion. To cultivate a strong brand image and corporate reputation, companies leverage supply chain innovations to more effectively meet their environmental and social responsibilities and enhance their internal governance. Conversely, in less competitive sectors, state-owned supply chain core enterprises possess greater bargaining leverage in collaboration with upstream and downstream companies, and they lack motivation to modify their business plans due to their market dominance.

8. Conclusions

This article selects all state-owned listed companies from 2009 to 2023 as the initial research sample to examine the impact of SOEs’ ESG performance on the ESG performance of enterprises in the supply chain and the heterogeneity differences. The study shows the following: (1) The ESG performance of state-owned supply chain core enterprises has a significant positive impact on the ESG performance of companies in the supply chain, whereas non-state-owned supply chain core enterprises do not have this effect. (2) Furthermore, research indicates that this effect is asymmetric; when the supply chain core enterprise is an SOE and the enterprises in the supply chain are non-state-owned, the leading effect is more pronounced, and this effect is more powerful for upstream enterprises. (3) Heterogeneity tests found that the leading effect of ESG performance in state-owned supply chain core enterprises is more significant in those with larger scale, longer listing periods, and higher market competition.

The study contributions of the aforementioned findings and their significance to previous studies are primarily as follows: First, this article broadens the investigation into the determinants affecting the improvement of corporate ESG performance. This article innovatively examines the pivotal role of SOEs and investigates the determinants that enhance the ESG performance of enterprises within the supply chain, building upon the research of corporate governance and R&D activities [9,36]. Second, this study enhances the existing research on SOEs. Current research predominantly emphasizes SOE reform and high-quality development [77,78,79], with limited literature addressing the value features of SOEs in influencing the ESG performance of other enterprises. This article thoroughly examines the primary influence and variability of SOEs in improving the ESG performance of companies within the supply chain by categorizing the equity structure of enterprises in the chain. Third, it elucidates the uneven attributes of the ESG leadership influence of SOEs. This article reveals that the impact of the ESG performance of SOEs is asymmetric between upstream and downstream entities within the supply chain. This discovery not only contributes to the theoretical understanding of the green transmission effect of ESG in the supply chain but also holds significant practical implications for enhancing the ESG performance of enterprises.

This article includes research deficiencies, which also present prospects for further investigation in subsequent publications. This article employs an empirical research approach; however, qualitative research through case studies highlighting individual traits is also crucial, in addition to theoretical and quantitative research that demonstrates universal patterns. Eventually, we aspire for ESG to scientifically and logically assess corporate performance, positively influence managerial behavior in favor of shareholders, and enhance shareholder value. SOEs are distinctive regarding business operations, human resources, technology, management practices, and corporate culture. A standardized ESG evaluation can only provide a preliminary assessment; so, enhancing case analysis of unique enterprise groups is essential for refining. This not only facilitates the improvement of the ESG performance of SOEs but also aids in the acceptance and dissemination of ESG management practices tailored to their specific characteristics.