Policy-Driven Supply Chain Digitalization and Corporate Sustainability: Evidence from China’s Innovation Pilot

Abstract

Grounded in the Supply Chain Innovation and Application Pilot Policy, this study examines listed companies on China’s A-share market from 2009 to 2023, employing a difference-in-differences model to analyze the impact of supply chain digitalization on corporate ESG performance and its underlying mechanisms. The findings indicate that supply chain digitalization facilitates the improvement of corporate ESG performance by alleviating financing constraints, promoting digital technology innovation, and optimizing human capital structure. Heterogeneity analysis reveals that this effect is more pronounced in firms with greater board diversity, CEO–Chair duality, and higher market attention. This study extends the existing body of research on supply chain digitalization and thereby provides robust empirical evidence for policymakers and corporate managers to further promote enhanced ESG performance and advance sustainable development initiatives.

1. Introduction

Industrialization and economic advancement have engendered profound environmental challenges, attracting substantial attention to environmental preservation and sustainable development [1]. The Environmental, Social, and Governance (ESG) framework, formally introduced by the United Nations Environment Programme (UNEP) in 2004, posits that corporations must integrate ESG considerations—specifically environmental protection, social accountability, corporate governance responsibility—into their operational paradigms. The subsequent adoption of the UN 2030 Agenda for Sustainable Development in 2015 intensified global commitments to sustainability. Within this context, corporate ESG performance has emerged as observable evidence for investors and researchers attesting to firms’ dedication to sustainable practices, thereby attracting substantial scholarly and institutional attention [2]. Sound ESG performance is recognized as a catalyst for green technological innovation and a driver of low-carbon economic transformation. Consequently, the issue of advancing enterprise ESG performance to achieve sustainable development has become a critical research and policy imperative.

Recognizing the severe impact of traditional industrial production on environment, Germany pioneered the concept of Industry 4.0 in 2011. This concept entails the digital transformation of entire industrial sectors and consumer markets [3], supporting environmental sustainability through sustainable energy, resource efficiency, and carbon reduction [4]. Digital transformation of supply chains constitutes a fundamental component in the shift toward the Fourth Industrial Revolution. It facilitates information exchange and supply chain integration, reducing information distortion between upstream and downstream partners and optimizing resource allocation efficiency. Supply chain digitalization allows enhanced communication, collaboration, and data sharing among customers, suppliers, and service providers. Via the convergence of varied data inputs and the deployment of novel technical systems and management methodologies [5,6,7,8], supply chain digitalization is widely regarded as a promising field [9,10]. Thus, as a pioneering technological enabler of Industry 4.0, supply chain digitalization has become one of top priorities within global commerce, exerting large influence on corporate process optimization and Environmental, Social, and Governance (ESG) performance metrics, spanning environmental management systems, social accountability mechanisms, and corporate governance practices [11,12,13,14,15,16,17]. However, scholarly investigation into the causal relationship between supply chain digitalization and ESG performance remains nascent. The precise nature of this relationship and its underlying mechanisms need further study.

China presents a unique empirical setting for examining supply chain digitalization’s impact on corporate ESG performance. First, supply chain digitalization advancement in China is predominantly driven by nation-directed policy interventions in recent decades. Illustratively, in the year of 2018, 8 governmental bodies comprising the Ministry of Commerce collectively published the Circular on Pilot Initiatives for Supply Chain Innovation and Application. These policies are formulated to establish an intelligent supply chain system and promote the digitalization, networking, and intellectualization of all segments within the supply chain. This affords unique opportunities for causal analysis of supply chain digitalization effects.

Within this institutional context, referring to the Supply Chain Application and Innovation Pilot Policy, this study examines the impact of supply chain digitalization on corporate ESG performance and its underlying transmission mechanisms using a sample of China’s A-share listed firms from 2009 to 2023. This study provides effective recommendations for promoting supply chain digitalization and facilitating corporate sustainable development. The findings indicate that supply chain digitalization facilitates the improvement of corporate ESG performance by alleviating financing constraints, promoting digital technology innovation, and optimizing human capital structure.

The subsequent sections of this study are structured in the following way: Section 2 reviews the pertinent literature; Section 3 introduces the policy context and proposes the main hypotheses; Section 4 identifies the variables and data sources, and presents the construction and elucidation of the Difference-in-Differences (DID) model; Section 5 reports the baseline regression results, including endogeneity tests, robustness checks, and heterogeneity tests; Section 6 conducts mechanism tests; Section 7 is the discussion; Section 8 provides the conclusions, policy recommendations, limitations, and future research directions.

2. Literature Review

Both digitalization and digital transformation represent advanced approaches to enhance production efficiency and strengthen competitiveness. Those approaches leverage new-generation information technologies such as cloud computing, big data, and the Internet of Things. Although these two concepts are highly susceptible to confusion, digitalization primarily focuses on how technologies like cloud computing and big data are used to transform existing business processes [18]. It emphasizes more on cost reduction and business process optimization [19], while digital transformation refers to the introduction of new business models through the implementation of novel business logic to create and capture value [20]. This study underscores the use of new-generation information technologies to transform supply chain digitalization in the existing business processes, and adopts SCD as the abbreviation for supply chain digitalization.

SCD has become a critical focus area in operations management study, bearing considerable ramifications for supply chain optimization, operational outcomes, and long-term viability [13,15]. This paradigm shift harnesses cutting-edge innovations including large-scale data analysis, machine intelligence, interconnected sensor networks, and distributed ledger technology to reconfigure supply network operations and competencies [21,22]. Through the strategic incorporation of these technological innovations, enterprises can attain enhanced transparency, operational autonomy, and adaptive capacity across their supply networks [23]. Research reveals that, from an operational standpoint, digitalization of supply chains provides multiple strategic benefits. First, it facilitates enhanced predictive capabilities for demand patterns and stock-level refinement via data-driven analysis, resulting in cost minimization and elevated customer service performance [24]. Second, it permits instantaneous product surveillance and authentication across the supply network, augmenting operational transparency and diminishing exposures to deceptive practices or compliance breaches [7,25]. Third, it fosters the creation of more flexible and adaptive supply network structures capable of rapid adjustment to market fluctuations or operational interruptions [26].

ESG represents a corporate value system focusing on a company’s environmental, social, and governance performance rather than financial outcomes, concretely embodying its sustainability philosophy. Implementing an ESG strategy benefits a firm’s long-term development [27]. Therefore, many scholars have begun exploring the influencing factors of ESG.

Regarding research on influencing factors, studies primarily focus on corporate structure, executive attributes, and the broader institutional and market context. Numerous investigations concentrate on the influence of corporate structure on firms’ ESG performance, exemplified by firm size [28], board structure [29], controlling shareholder pledging [30], and multiple large shareholders [31]. Concerning executive attributes, some scholars have substantiated the significance of CEO type [32], CEO inside debt [33], CEO gender [34], career concerns [35], and managerial ability [36] for corporate ESG performance. Furthermore, several researchers have investigated how external market conditions and institutional frameworks shape corporate ESG performance. Research reveals that factors including institutional investors [37], rating agencies [38], stakeholders [39], and market competition [40,41] all influence firms’ decisions to implement ESG practices. Contemporary advancements in digital and artificial intelligence technologies are progressing rapidly, with numerous scholars considering artificial intelligence technologies and digitalization to be pivotal factors influencing corporate ESG performance [42,43]. Scholars pointed out that digital transformation can enhance corporate ESG performance through improving green innovation capabilities [44] and increasing capacity utilization [45].

A burgeoning research stream examines the interconnection between supply chains digitalization and corporate ESG performance. Scholarly evidence reveals that SCD serves as a pivotal enabler for corporate sustainability attainment. Digitalized supply chains can improve sustainability outcomes by optimizing material flows, minimizing environmental footprints, and strengthening occupational health protections [46,47]. Simultaneously, studies find that green technology innovation [48], external supervision [49], Organizational Synergy and management incentives [50] are also effective pathways. Scholars have explored variations in how supply chain digitization correlates with ESG performance in the field of regional marketization levels [51], innovation capabilities [49], and inter-industry variations [49].

Existing studies exhibit several deficiencies. On the one hand, the majority of studies discussing corporate ESG performance predominantly concentrate on internal corporate traits and the external environment; the literature analyzing the effect of SCD on corporate ESG performance remains relatively scarce. On the other hand, existing studies have not investigated the mediating effects of enterprise digital technology innovation and human capital structure adjustment induced by SCD within the impact mechanism. In addition, within existing research, explorations of heterogeneity pay more attention to firms’ external environments, and there is a lack of studies from a corporate governance perspective.

The principal scholarly additions of this research paper are mainly manifested in the following two levels: Theoretically, while extant literature has examined the influences of digital technology applications—such as digital finance, corporate digital transformation, and intelligent manufacturing—on corporate ESG performance, it has overlooked the potential impact of Supply chain digital construction, characterized by “data integration, resource sharing, and business collaboration,” on corporate ESG performance. Furthermore, the metrics for digital technology used in these studies may inadequately address endogeneity issues. In contrast, this paper leverages the natural exogenous event of the Supply Chain Innovation and Application Pilot Policy to distinctly identify the impact of SCD on corporate ESG performance, enriching studies examining influential elements influencing corporate ESG performance. Moreover, based on the perspectives of financing constraints, digital technological innovation, and human capital structure optimization, this paper reveals the micro-level mechanisms through which SCD affects corporate ESG performance, thereby further augmenting the theoretical framework of SCD. From the practical perspective, based on the investigation of corporate governance, this study investigates the heterogeneous impacts of supply chain digitization on firm-level ESG performance through a three-dimensional analysis framework: board diversity, CEO duality, and market attention. The research contributes fresh understanding to motivate corporate toward ESG excellence, refine ESG frameworks, develop sustainable competitive advantages for enterprises in China, and maintain financial market equilibrium, which is of significant regulatory recommendations.

3. Institutional Background and Research Hypotheses

3.1. Institutional Background

ESG practices specific to the Chinese market are predominantly policy-driven and are profoundly influenced by the institutional environment in which enterprises operate. Notably, propelled by the ongoing technological and industrial revolution, the accelerated advancement of digital technologies has facilitated the emergence of a novel supply chain model—the digital supply chain. As a defining characteristic of contemporary supply systems, the digital supply chain fundamentally entails the enhancement of management, operation, optimization, and decision-making capabilities for enterprises situated at network nodes via implementation of digital innovations. To further accelerate the horizontal extension and vertical interaction of the digital supply chain, the Ministry of Commerce (MOC) and seven other ministries formally released the Circular on Launching the Supply Chain Innovation and Application Pilot Program in 2018. Through evaluation, 55 urban testbeds and 266 business entities engaged in innovative logistics applications were successively identified. In response to the escalating public health crisis COVID-19 and the intensification of Sino-US trade frictions, in 2020, to better leverage the important role of the supply chain innovation and application pilot program in stabilizing global supply chains and promoting the resumption of work and production, the MOC and seven other ministries jointly issued the Circular on Further Advancing the Supply Chain Innovation and Application Pilot Program. In subsequent years, central and local governments successively introduced policy documents concerning the application, performance evaluation indicators, and operational norms for model cities and model enterprises. The formulation, refinement, and implementation of these policies have played an increasingly prominent role in driving premium economic growth and enhancing the modernization level of industry supply chains.

3.2. Research Hypotheses

3.2.1. The Impact of SCD on Corporate ESG Performance

The Supply Chain Innovation Initiative cultivates a comprehensive and high-performing industrial ecosystem through three strategic dimensions: pioneering supply chain modernization, aggressively promoting sustainable logistics networks, and proactively engaging with international value chains. It holds significant importance for promoting supply chain upgrades, enhancing efficiency, driving industrial transformation, strengthening risk resilience, and boosting economic development. SCD, through integrating advanced digital technologies including the Internet of Things, big data analytics, blockchain, and artificial intelligence, can influence a company’s environmental, social, and governance (ESG) performance [50]. First, according to reverse logistics theory, SCD instantaneous surveillance and enhancement of manufacturing processes and logistics processes through technologies like the Internet of Things and big data analytics [52]. This helps enhance resource utilization effectiveness, minimize carbon footprints and waste generation, thereby lowering environmental burdens and resource consumption, and significantly enhancing environmental performance. Second, based on green supply chain management theory, digital technologies can also facilitate green production and logistics. On one hand, they enable comprehensive monitoring and analysis of the entire production process through digital means, identifying energy anomalies to minimize resource waste and environmental pollution [53]. On the other hand, SCD allows instantaneous monitoring and scheduling of logistics transport, reducing empty loads and shortening transportation distance and time, which decreases energy consumption and carbon emissions [54], thereby improving overall green innovation performance. Moreover, according to supply chain management theory, SCD enhances a company’s ability to collaborate with upstream and downstream partners, increasing supply chain transparency and compliance. This not only helps companies better monitor and manage suppliers’ environmental and social performance, ensuring supply chain sustainability and enabling more effective fulfillment of social responsibilities and governance requirements, but also elevates their own ESG disclosure levels. Concurrently, digitalization fosters greater clarity and effectiveness in managerial decision processes, contributing to superior corporate oversight frameworks. Based on the preceding analysis, as illustrated in Figure 1, we propose the following verifiable hypotheses:

H1. 

SCD can boost corporate ESG performance.

3.2.2. Financing Constraints Mechanism

In accordance with the resource dependence theory, financing constraints, as a crucial limiting factor in corporate resource allocation, can prompt enterprises to more judiciously choose strategic investment directions and meticulously allocate scarce resources. Nevertheless, they may also severely impede firms’ capabilities to seize major strategic opportunities, including the adoption and integration of disruptive technologies and business models, owing to resource scarcity. SCD catalyzes a fundamental transformation in corporate information systems and technology infrastructure [55]. This shift necessitates substantial financial resources for the procurement of new hardware and software, talent cultivation, process reengineering. Additionally, it also bears potential transformation disruptions and integration risks.

First, impact of SCD on financing constraints. According to information economics and transaction cost theory, enterprises’ financing constraints mainly arise from three fundamental factors: asymmetric information, substantial transaction costs, and complexities in valuing and pledging assets. SCD provides robust technological support and an ecological foundation for addressing these issues. It achieves this by reshaping information flows, enhancing transaction transparency [56], and activating asset value. The deep connectivity, data sharing, and process visualization enabled by SCD effectively reduce information acquisition costs and transaction friction [57]. On one hand, SCD deeply embeds underlying data technologies. This enables real-time, accurate, and immutable recording and transmission of core operational information throughout business processes. This breakthrough overcomes traditional “information silos” [58], substantially enhancing the transparency and verifiability of corporate operational information. On the other hand, SCD constructs a collaborative network platform with multi-party participation and high information sharing [59]. This platform serves not only as an operational optimization tool but also as a powerful collaborative financing ecosystem. This breaks down information silos and reduces the uncertainty and complexity of information disclosure [60]. Financial institutions can directly verify the authenticity of underlying transactions [61]. It uses platform data and precisely matches financing supply with demand through efficient online process integration. This enhances financing efficiency.

Second, the involvement of financing constraints in SCD’s promotion of corporate ESG performance. The extent of financing constraints faced by enterprises directly affects their financial capacity. This limits their ability to deploy and utilize advanced digital technologies, optimize processes, and undertake long-term ESG investments. As a result, the actual effectiveness of SCD in empowering ESG is constrained. Specifically, regarding the environmental dimension, enterprises engaged in green technology innovation activities face higher risks, longer payback periods, and greater dependence on capital [62]. Severe financing constraints often lead firms to reduce investments in green technology innovation. Through green technology innovation, firms with strong financing capabilities can achieve refined management and source control of energy consumption and waste emissions across the supply chain. This can significantly reduce environmental burdens and provide fundamental assurance for improving environmental performance [63]. Regarding social responsibility, the alleviation of financing constraints provides financial assurance for enterprises to fulfill their social responsibilities [64]. This enables firms to allocate resources more effectively to key areas. Thes areas includes but are not limited to employee well-being enhancement, community relationship maintenance, and supply chain responsibility supervision. In this way, companies will significantly strengthen social performance and brand reputation. Regarding corporate governance, establishing and optimizing robust supply chain digital risk control systems and their underlying data infrastructure requires substantial capital investment. Well-funded enterprises can deploy intelligent risk control networks spanning the entire supply chain. This will enhance risk prediction capabilities and the timeliness of governance responses. Building upon these findings, as illustrated in Figure 1, we postulate the subsequent research hypothesis:

H2. 

SCD can promote the improvement of corporate ESG performance by alleviating financing constraints.

3.2.3. Digital Technology Innovation Mechanism

From the perspective of the resource-based framework and strategic transformation theory, the core resources and capabilities held by enterprises function as a double-edged sword. They can catalyze organizational self-adjustment in dynamic environments and strategic realignment to meet external demands, yet may also create resistance to new technology integration and strategic reshaping due to inherent inertia and path dependence. Digital technology innovation, as one of the core competencies accumulated and formed during corporate digital transformation, serves as a significant engine for firms to ultimately accomplish sustainable development strategic goals [65]. First, the influence of SCD on corporate digital technology innovation. According to the innovation theory, digital technology innovation exhibits characteristics of plasticity, editability, openness, and transferability. This implies that the development of digital products permits multi-party participation, and the functionalities can be continuously adjusted and optimized during the development and implementation phases [66]. Based on communication and trust, SCD effectively promotes information cooperation and sharing. Consequently, it enables knowledge creation, flow, transfer, and spillover activities supply chain enterprises. These activities generate a synergistic innovation effect that exceeds the sum of individual contributions (“1 + 1 > 2”) [67]. From one perspective, SCD compels firms to embed smart technological solutions into the fundamental architecture of supply chain operations. This is conducive to mining large-scale, deep-seated unstructured data. It assists upstream and downstream participants in breaking through the “information silos”. Furthermore, it promotes the visualization and interconnectivity of supply chain management paradigms [58]. By leveraging supply chain capabilities, enterprises can accumulate resources for technological advancement in digital domains. At the same time, they can align provider capabilities with market needs. This improves the targeting accuracy of digital technology innovation [68]. It also facilitates collaborative information sharing and joint decision-making among firms and their supply chain collaborators (both upstream and downstream).

Second, the impact of digital technology innovation on improving organizational ESG performance via SCD. SCD is fundamentally a systematic transformation that deeply integrates digital technologies into the entire supply chain management process. As a highly technology-integrated form of advancement, it inherently necessitates enterprises to master and apply advanced digital technologies for empowerment. Specifically, within the environmental dimension, digital technology innovation serves as the technological foundation for boosting green productivity across supply chains and accomplishing green transformation [69]. Robust digital technology innovation capabilities empower enterprises to develop and implement more advanced Internet of Things sensors, artificial intelligence (AI) algorithms, and big data analysis tools. These technologies facilitate instantaneous, accurate supervision and dynamic optimization of energy consumption, material flows, and waste emissions. Such monitoring and improvement occur throughout all stages of the supply chain. Regarding societal accountability, innovative applications grounded in digital technologies can significantly enhance the connectivity and interaction between enterprises and their stakeholders [65]. Innovative technological platforms have the potential to augment the transparency and visibility of information. This information relates to working conditions and labor rights for supply chain workers. This, in turn, compels enterprises to carry out their social commitments in the supply chain. In the realm of corporate governance, cutting-edge digital technological innovations endow enterprises with unparalleled governance insights and control capabilities. These innovations include advanced data analytics, artificial-intelligence-driven-risk early-warning models, and smart contracts. They enable firms to operate at unprecedented speed [70]. These technologies can conduct in-depth analyses of extensive supply chain data to precisely identify potential compliance risks, operation risks, and reputation risks. This provides robust technical support for sound and efficient corporate governance. Based on the preceding analysis, as illustrated in Figure 1, we propose the following verifiable hypotheses.:

H3. 

SCD can facilitate the enhancement of corporate ESG performance through digital technological innovation.

3.2.4. Human Capital Structure Optimization Mechanism

Grounded in the resource-based view and strategic transformation theory, the resources and capabilities of enterprises can enable rapid self-adjustment and realign organizational strategies with the external environment. However, they may also impede the successful assimilation of innovative technologies with corporate strategic frameworks [71]. Human capital, as one of the most crucial strategic resources of enterprises, acts as the primary entity for activities such as R&D innovation, production, service marketing, and various competitive strategic selections.

First, impact of SCD on the optimization of corporate human capital structure. From the perspective of optimizing labor factors, SCD elevates production complexity and demonstrates a marked skill preference for workforce expertise [72]. The trends of supply chain collaboration, servitization, and intelligence are driving the adoption of advanced technologies—from intelligent human–computer interaction to smart factories. These steadily supplant simple, repetitive tasks and reshape the enterprise labor landscape. This significantly curtails operational positions, realizing the substitution of digital technologies for low-skilled human capital [73]. For example, the research team led by Acemoglu (2020) put forward that industrial robots can replace workers in specific tasks [74]. These tasks include but are not limited to welding, palletizing, and assembling. As a result, labor demand in manufacturing processes is reduced [74]. Furthermore, SCD gives rise to new business models, including information manufacturing, intelligent logistics, and supply chain finance. This leads to an increase in cognitively demanding operations and analytical decision-making processes. A significant percentage of these activities require in-depth evaluation and sophisticated handling by specialized professionals to create organizational value [75]. This, in turn, strengthens the competitive advantage of highly skilled workers. For instance, Wang points out that, owing to the characteristics of artificial intelligence technology, it substitutes low-skilled labor involved in repetitive and procedural tasks [76]. In contrast, labor in technical and R&D positions is more adaptable to complex new roles. These positions demand stronger technical and innovative capabilities. This group especially includes researchers with complex innovative knowledge and creative abilities, as well as service personnel with sophisticated communication and marketing skills [77]. Consequently, under the dual impacts of “destructive substitution” and “complementary creation,” the digitalization of the supply chain gradually modifies enterprises’ demand structure for low-skilled and high-skilled human capital.

Second, the role of human capital structure optimization in the promotion of corporate ESG performance by SCD. SCD is a skill-biased technological transformation. It objectively necessitates enterprises to possess a significant amount of high-quality human capital. This human capital must have professional knowledge and digital skills to serve as fundamental support. High-skilled human capital demonstrates higher marginal productivity and can fully exert its external effects, thereby enhancing corporate ESG performance. Specifically, in the environmental dimension, highly educated and skilled employees within enterprises can recognize the long-term costs associated with environmentally harmful actions [78]. They leverage their excellent innovative capabilities and knowledge reserves. This allows them to develop more advanced environmental technologies and products, optimize production processes and resource allocation, and enhance energy efficiency. As a result, environmental pressures is alleviated. In terms of social responsibility, a well-structured human capital helps enterprises attract and retain outstanding talents, leading to the improvement of employee welfare and rights protection. Regarding corporate governance, professional talents and experienced employees can provide more professional insights and recommendations in governance, contributing to more scientific and effective decision-making. Drawing from the preceding analysis, as illustrated in Figure 1, we advance the subsequent testable hypothesis:

H4. 

SCD can promote the improvement of corporate ESG performance by optimizing human capital structure.

4. Research Design

4.1. Model Design

This paper treats the Supply Chain Innovation and Application Pilot policy as external policy shock. Based on the study by Wang et al. [79], it employs a difference-in-differences (DID) model to examine the impact of SCD on corporate ESG performance. Based on this, the following difference-in-differences (DID) model is constructed:

ESG_it = α + βTreat_i × Time_i + γcontrol_it + δ_it + μ_it + ε_it

(1)

In this model, the subscripts i and t signify the enterprise and year, respectively. ESG_it stands for the ESG performance of enterprise i in year t. Treat_i serves as a dummy variable for the treatment group: it assumes a value of 1 if the enterprise is incorporated into the supply chain innovation and application pilot list; otherwise, it takes a value of 0. Time_i is a dummy variable for the pilot period, with a value of 1 after 2018 (post-pilot) and 0 before 2018 (pre-pilot). control_it denotes the set of control variables. δ_it and μ_it represent the fixed effects for enterprises and years, respectively. α is the constant term, and ε_it is the random error term. The coefficient β of the interaction term Treat_i × Time_i is the key estimation coefficient in this research, representing the net impact of SCD on corporate ESG performance.

4.2. Variable Definition

4.2.1. Dependent Variable

Corporate ESG Performance (ESG). This research employs the Hua Zheng ESG Rating to measure corporate ESG performance. Compared to ESG assessments conducted by institutions such as SynTao Green Finance, Social Value Investment Alliance, and Wind, the Hua Zheng ESG Rating offers a longer sample time span and a more comprehensive evaluation framework. The framework incorporates 14 distinct thematic dimensions, 26 core assessment elements, and more than 130 granular measurement parameters. These span across Environmental Management System, Green Operation Objectives, Green Products, External Environmental Certifications, Environmental Violation Incidents, Institutional Systems, Health and Safety, Social Contribution, Quality Management, Institutional Development, Governance Structure, Business Activities, Operational Risks, and External Disciplinary Actions, demonstrating closer alignment with the Chinese market context.

4.2.2. Explanatory Variables

SCD (Treat_i × Time_i). Although supply chain innovation and application pilot initiatives encompass two aspects, enterprises and cities, the principal objective of pilot cities is to offer policy backing for the innovative development of the supply chain and establish a conducive operational environment. As nodes within the supply chain, enterprises are more apt to carry out research and design via enterprise-level pilot programs. Specifically, Treat is a dummy variable denoting the treatment groups: Treatment = 1 when an enterprise participates in supply chain innovation and application pilot initiatives, and Treatment = 0 otherwise. Time is a dummy variable representing the pilot periods: Time = 0 prior to 2018, and Time = 1 subsequent to 2018.

4.2.3. Control Variables

Drawing upon extant research regarding corporate governance and corporate ESG [12,80,81], this paper controls for the subsequent variables in the regression: Shareholding Ratio of the Largest Shareholder, Board Size, Proportion of Independent Directors, Asset/Liability Ratio, Fixed Assets Ratio, and Return on Total Assets.

4.3. Sample Selection and Data Sources

This study employs panel data of Chinese A-share listed companies from 2009 to 2023 as the research sample. The fundamental data were retrieved from National Intellectual Property Administration, China Stock Market and Accounting Research (CSMAR) database, and the Wind database. The list of pilot enterprises was compiled in accordance with the Ministry of Commerce’s Circular on Announcing the List of National Supply Chain Innovation and Application Pilot Cities and Pilot Enterprises. In terms of data cleaning, the initial sample was subjected to the following treatments: observations labeled as ST, *ST, PT, or those belonging to the financial industry were excluded; and data for observations were winsorized at both ends at 5 percentiles. After implementing these screening procedures, a final sample of 41,129 firm-year observations was acquired. The definitions and calculation methods of the main variables are shown in

Table 1. Definition and calculation method of main variables.

Variable Type	Variable Name	Variable Symbol	Variable Declaration
Dependent Variable	Corporate ESG performance	ESG	Huazheng’s comprehensive score on environment, society and governance
Explanatory Variables	SCD	DID_Enterprise	The enterprise is located in a pilot city and has a value of 1 for the year 2018 and beyond, otherwise it is 0
Control variables	Shareholding Ratio of the Largest Shareholder	Top1	Number of shares held by the largest shareholder/total number of shares
	Board Size	Board	Number of directors on the board
	Proportion of Independent Directors	Indpe	Number of independent directors/Board Size
	Asset/Liability Ratio	Lev	Total liabilities/Total assets
	Fixed Assets Ratio	Fixed	Fixed assets/Total assets
	Return on Total Assets	Rota	Retained profits/Total assets balance

. The descriptive statistics of the main variables are presented in

Table 2. Descriptive Statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
DID_Enterprise	41,129	0.0140	0.1175	0	1
ESG	41,129	73.0432	4.5408	63.19	81.38
Top1	41,129	34.1067	14.8913	1.84	89.99
Board	41,129	8.4998	1.6981	2	18
Indpe	41,129	37.5301	4.9630	33.33	50
Lev	41,129	43.2687	19.8488	10.8735	78.8377
Fixed	41,129	20.9224	14.8673	1.27	53.15
Rota	41,129	0.0352	0.0481	−0.0791	0.1258

.

5. Empirical Findings and Analysis

5.1. Benchmark Regression Outcomes

The benchmark regression outcomes regarding the impact of SCD on corporate ESG performance are presented in

Table 3. Benchmark Regression Outcomes.

Variable	ESG
	(1)	(2)
DID_Enterprise	0.9280 *** (2.75)	0.9302 *** (2.80)
Top1		0.0126 *** (2.84)
Board		0.1189 *** (3.53)
Indpe		0.0655 *** (7.32)
Lev		−0.0238 *** (−8.96)
Fixed		−0.0146 *** (−3.71)
Rota		3.7714 *** (5.60)
Enterprise fixed effect	YES	YES
Year fixed effect	YES	YES
Obs	41,129	41,129
R2	0.5281	0.5353

Note: t statistics in parentheses. *** p < 0.01.

. Column (1) presents the regression results excluding all control variables, whereas Column (2) exhibits the results subsequent to the inclusion of all control variables. It is discernible that the estimated coefficients of interaction term DID_Enterprise in Columns (1) and (2) both demonstrate positive at the 1% significance level. This implies that, when other factors are controlled for, the enhancement of SCD significantly promotes corporate ESG performance, thereby validating Hypothesis H1.

5.2. Robustness Test

5.2.1. Pre-Trend Test

A fundamental assumption of the Difference-in-Differences (DID) model posits that both the treatment group and the control group display identical trend patterns prior to policy implementation. Adopting the approach proposed by Hong et al. [82] for testing parallel trend assumptions, this research defines a series of dummy variables. Specifically, Pre_i represents the year preceding the implementating of the supply chain innovation and application pilot policy, and post_i denotes the year following its implementation, with 2018 being the policy implementation year. As depicted in

Table 4. Pre-trend test results.

Time	Coefficient	[95% Conf. Interval]
pre_5	0.1568 (0.30)	−0.8762	1.1898
pre_4	0.1621 (0.30)	−0.9113	1.2355
pre_3	0.3453 (0.87)	−0.4357	1.1263
pre_2	0.4134 (1.14)	−0.3002	1.1270
current	1.6876 *** (4.60)	0.9684	2.4068
post_1	2.1714 *** (5.79)	1.4376	2.9052
post_2	2.2866 *** (4.04)	1.1781	3.3951
post_3	1.8517 *** (3.05)	0.6617	3.0417
post_4	1.4897 *** (2.84)	0.4629	2.5165
post_5	2.0013 *** (3.92)	1.0012	3.0014

Note: t statistics in parentheses. *** p < 0.01.

and Figure 2, prior to the policy shock, there was no significant disparity in Environmental, Social, and Governance (ESG) performance between the experimental group and the control group, which is consistent with the parallel trend hypothesis. In 2018, the supply chain innovation and application pilot policy exhibited significant promotional effects. In subsequent years, it continuously released policy dividends and maintained a sustained positive influence on corporate ESG performance.

5.2.2. PSM-DID Test

In order to account for endogeneity problems that might emerge due to selection bias, our robustness analysis applied the difference-in-differences method with propensity score matching (PSM-DID), further validating the relationship between SCD and corporate ESG performance. Specifically, using all control variables as covariates, nearest neighbor matching was performed between the treatment and control groups via propensity score matching (PSM). Given the relatively smaller number of treatment group observations compared to the control group, each treatment group observation was matched with five control group observations exhibiting the closest propensity scores. Compared to a 1:1 matching ratio, the 1:5 ratio utilizes more information from the control group, enhancing estimation efficiency and reducing estimator variance. Post-matching balance tests were conducted, with results presented in

Table 5. Balance test results.

Variable	Unmatched Matched	Mean		%Bias	%Reduct |Bias|	t-Test
		Treated	Control			t	p
Top1	Unmatched	35.354	34.07	8.8	73.6	2.93	0.003
	Matched	35.354	35.693	−2.3		−0.54	0.589
Board	Unmatched	9.0979	8.4821	32.3	97.2	12.32	0.000
	Matched	9.0979	9.0808	0.9		0.20	0.841
Indpe	Unmatched	37.503	37.531	−0.5	94.0	−0.19	0.851
	Matched	37.503	37.505	0.0		−0.01	0.994
Lev	Unmatched	52.988	42.98	51.9	98.2	17.17	0.000
	Matched	52.988	53.168	−0.9		−0.23	0.815
Fixed	Unmatched	19.774	20.956	−7.7	96.5	−2.70	0.007
	Matched	19.774	19.815	−0.3		−0.06	0.949
Rota	Unmatched	0.0426	0.0350	16.1	96.3	5.36	0.000
	Matched	0.0426	0.0423	0.6		0.15	0.880

. All variables demonstrated a bias below 5%, and all p-values exceeded 0.1, indicating no significant differences in observable variables between the treatment and control groups after matching. Subsequently, the Difference-in-Differences (DID) method was applied to the matched sample for regression analysis. Regression results, shown in

Table 6. PSM-DID test results.

Variable	ESG
DID_Enterprise	0.7102 ** (1.96)
Top1	0.0063 (0.60)
Board	0.0707 (1.13)
Indpe	0.0654 *** (3.21)
Lev	−0.0358 *** (−4.68)
Fixed	−0.0147 (−1.32)
Rota	2.2282 (0.98)
Enterprise fixed effect	YES
Year fixed effect	YES
Obs	6436
R2	0.7083

Note: t statistics in parentheses. ** p < 0.05, *** p < 0.01.

, reveal that the regression coefficient of the core variable remains significantly positive, robustly supporting the research hypothesis proposed in this paper.

5.2.3. Placebo Test

The use of the difference-in-differences method also necessitates considering whether the baseline estimation results are influenced by other random factors. To address this, drawing on Chen et al. [83], this paper employs a temporal placebo test to identify the randomness of the Supply Chain Innovation and Application Pilot policy. The policy shocks were lagged by 1 to 4 years, respectively, and pseudo-treatments were conducted using 2017, 2016, 2015, and 2014 as reference points for the DID estimation.

Table 7. Temporal Placebo test results.

DID_Enterprise Number of Lags	Pseudo-Processing Time	Coefficient	Std. Err.	p-Value	[95% Conf. Interval]
1	2017	0.5639	0.6883	0.103	−0.0662	1.1940
2	2016	0.2478	0.6232	0.499	−0.4714	0.9670
3	2015	0.2587	0.6133	0.509	−0.5092	1.0266
4	2014	0.4747	0.6375	0.279	−0.3844	1.3337

reports the outcomes of the temporal placebo test, including the estimated coefficients of the placebo effect, clustered robust standard errors, p-values, and 95% confidence intervals. All placebo effect p-values exceed 0.1, thus accepting the null hypothesis that “the placebo effect is zero.” More intuitively, Figure 3 displays the 95% confidence intervals for the temporal placebo effects. All confidence intervals cover zero, indicating the placebo test was passed.

5.2.4. Exclusion of Other Policy Interference

Further efforts were made to exclude other policies potentially affecting corporate ESG performance. First, regarding intelligent manufacturing pilot policies, firms can reallocate the substantial subsidies obtained toward SCD initiatives. As a result, financing constraints is alleviated and subsequently ESG performance is enhanced. Second, concerning data factor marketization policies, these may unleash the multiplier effect of data factors, optimize resource allocation, and drive collaborative energy-saving across supply chains. This will ultimately improve corporate ESG performance. Third, for big data comprehensive pilot zones, their establishment fosters conducive digital ecosystems. This will accelerate enterprise SCD, leading to measurable ESG improvements. To mitigate interference from these three policies, dummy variables for each policy were individually incorporated into the baseline model for control. As shown in

Table 8. Test outcomes isolating influence from other policies.

Variable	ESG
	(1)	(2)	(3)	(4)
DID_Enterprise	0.7658 ** (2.29)	0.9153 *** (2.80)	0.9197 *** (2.84)	0.7762 ** (2.32)
Intelligent Manufacturing Pilot policy	1.5523 *** (4.14)			1.5948 *** (4.23)
Data Factor Marketization policy		0.2874 *** (2.67)		0.1805 (1.60)
Big Data Comprehensive Pilot Zone policy			0.4221 *** (3.33)	0.3748 *** (2.81)
Top1	0.0090 ** (2.00)	0.0084 ** (1.87)	0.0088 ** (1.96)	0.0092 ** (2.05)
Board	0.0908 *** (2.71)	0.0901 *** (2.69)	0.0910 *** (2.72)	0.0924 *** (2.77)
Indpe	0.0638 *** (7.08)	0.0640 *** (7.09)	0.0636 *** (7.05)	0.0632 *** (7.02)
Lev	−0.0210 *** (−7.85)	−0.0213 *** (−7.90)	−0.0214 *** (−7.96)	−0.0215 *** (−8.01)
Fixed	−0.0140 *** (−3.53)	−0.0140 ** (−3.52)	−0.0138 *** (−3.47)	−0.0137 *** (−3.46)
Rota	4.5734 *** (6.47)	4.6044 *** (6.50)	4.6360 *** (6.56)	4.6745 *** (6.63)
Enterprise fixed effect	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES
Obs	39,271	39,271	39,271	39,271
R2	0.5262	0.5257	0.5259	0.5267

Note: t statistics in parentheses. ** p < 0.05, *** p < 0.01.

, it was found that the Supply Chain Innovation and Application Pilot policy continues to significantly promote corporate ESG performance. Furthermore, simultaneously controlling for all three aforementioned policies revealed that the significance of DID_enterprise remains statistically significant at the 5% level.

5.2.5. Replacement of the Dependent Variable

Currently, domestic third-party ESG rating databases exhibit systematic differences in their indicator systems, which have not yet been fully standardized and unified. Concurrently, the history of domestic ESG ratings is relatively short. Considering data availability, the Bloomberg ESG database, characterized by a longer data disclosure history and a relatively well-developed indicator system, was selected for robustness testing. The results, as shown in

Table 9. The result of replacing the dependent variable.

Variable	ESG_PB
	(1)	(2)
DID_Enterprise	3.2133 *** (3.78)	3.2334 *** (3.82)
Top1		−0.0088 (−0.69)
Board		0.1597 ** (2.08)
Indpe		0.0644 *** (2.79)
Lev		−0.0036 (−0.45)
Fixed		0.0089 (0.81)
Rota		8.9412 *** (4.72)
Enterprise fixed effect	YES	YES
Year fixed effect	YES	YES
Obs	13089	13,089
R2	0.8178	0.8190

Note: t statistics in parentheses. ** p < 0.05, *** p < 0.01.

, reveal that the significance of DID_enterprise remains statistically significant at the 1% level.

5.2.6. Adoption of Stricter Clustering Standards and Interactive Fixed Effects

Given potential significant disparities in economic development levels across industries, cities, and provinces, it is necessary to control for these factors to prevent potential omitted variable bias or heteroscedasticity issues. Based on the standard errors clustered at the firm level, this study further integrates Industry#year interactive fixed effects, City#year interactive fixed effects, and Province#year interactive fixed effects. The regression results presented in

Table 10. Test results using stricter clustering standards and interactive fixed effects.

Variable	ESG
	(1)	(2)	(3)	(4)
DID_Enterprise	1.0283 *** (3.05)	0.8003 ** (2.12)	0.8582 *** (2.60)	0.8529 ** (2.25)
Top1	0.0145 *** (3.32)	0.0116 ** (2.34)	0.0138 *** (3.12)	0.0154 *** (3.27)
Board	0.1208 *** (3.70)	0.1055 *** (2.84)	0.1130 *** (3.40)	0.1026 *** (2.90)
Indpe	0.0627 *** (7.20)	0.0641 *** (6.63)	0.0630 *** (7.09)	0.0580455 *** (6.26)
Lev	−0.0252 *** (−9.74)	−0.0240 *** (−8.31)	−0.0252 *** (−9.47)	−0.0256 *** (−9.28)
Fixed	−0.0174 *** (−4.54)	−0.0116 *** (−2.72)	−0.0134 *** (−3.44)	−0.0149 *** (−3.63)
Rota	3.8672 *** (5.76)	4.4201 *** (5.97)	4.1662 *** (6.22)	3.8071 *** (5.18)
Enterprise fixed effect	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES
Industry#year	YES	NO	NO	YES
City#year	NO	YES	NO	YES
Province#year	NO	NO	YES	YES
Obs	41,129	41,129	41,129	41,129
R2	0.5900	0.6107	0.5479	0.6607

Note: t statistics in parentheses. ** p < 0.05, *** p < 0.01.

indicate that the coefficient of DID_enterprise remains highly positive and statistically significant.

5.3. Heterogeneity Analysis

To delve deeper into the impact of SCD on the ESG performance of listed companies, this study analyzes the heterogeneous effects of SCD in enhancing corporate ESG performance across different scenarios. The analysis adopts a corporate governance perspective, focusing on board diversity, CEO duality, and market attention—key factors influencing corporate ESG decision-making.

5.3.1. Board Diversity and Corporate ESG Performance

Existing literature indicates that board diversity exerts a certain influence on corporate ESG performance [1]. Boards comprising members with diverse ages, genders, experiences, educational backgrounds, and professional expertise enable firms to consider issues more comprehensively and avoid entrenched mindsets. When formulating corporate strategic plans, their diverse perspectives, experiences, and professional backgrounds facilitate the precise identification of opportunities presented by policy resources. Consequently, board diversity provides both motivation and capability for firms to increase investments in sustainability and enhance focus on ESG performance. It is hypothesized that in enterprises with greater board diversity, SCD exerts a further pronounced effect on enhancing their ESG performance.

To verify the aforementioned hypothesis, this study refers to the method proposed by Kang et al. [84] to compute a comprehensive board diversity index for enterprises from five dimensions: age diversity, gender diversity, experience diversity, educational background diversity, and professional background diversity. The sample firms were grouped according to their comprehensive board diversity indices. A firm was categorized as having a relatively high board diversity index (Boa_high) if its index exceeded the median value of the comprehensive board diversity indices of all firms in the same industry during that year; otherwise, it was classified as having a relatively low index (Boa_low). The test results presented in

Table 11. The Heterogeneity Analysis Results of Board Diversity.

Variable	(1) Boa_High ESG	(2) Boa_Low ESG
DID_Enterprise	1.1510 *** (2.74)	0.5750 (1.01)
Top1	0.0080 (1.11)	0.0056 (2.41)
Board	0.0110 (0.22)	0.1546 *** (-8.75)
Indpe	0.0549 *** (4.19)	0.0639 *** (5.65)
Lev	−0.0276 *** (−7.20)	−0.0243 *** (−7.75)
Fixed	−0.0176 *** (−3.03)	−0.01270 *** (−2.91)
Rota	3.8201 *** (3.85)	1.7274 * (3.08)
Enterprise fixed effect	YES	YES
Year fixed effect	YES	YES
Obs	18,289	19,158
R2	0.5655	0.5786
Adj-R2	0.4907	0.5059
Fisher’s Permutation test	−0.576 *

Note: t statistics in parentheses. * p < 0.1, *** p < 0.01.

demonstrate that the coefficient of DID_enterprise in column (1) is 1.1510 and reach 1% significance level, while it is insignificant in column (2). Moreover, Fisher’s Permutation Test for inter-group coefficient differences yielded a significant outcome. This suggests that the enhancing effect of SCD on corporate ESG performance is more prominent among sample firms with a higher comprehensive board diversity index, thus validating our hypothesis.

5.3.2. CEO Duality and Corporate ESG Performance

Within corporate governance structures, CEO bears operational and administrative accountability for organizational activities, implements board resolutions, and develops as well as executes long-term strategies. The Chairman of the Board leads the board, convenes and presides over board meetings, represents the interests of shareholders, and determines the company’s strategic orientation. When these two roles are amalgamated (CEO duality), it effectively balances the relationships among internal stakeholders, alleviates the dual-agency problems, curtails managerial short-termism, enables a more efficient integration of corporate resources, and facilitates the implementation of strategies that are conducive to the long-term development of the company. Consequently, this steers managers to focus on sustainability objectives with long-term values, such as ESG performance. Therefore, it is hypothesized that in enterprises where CEO duality is practiced, SCD has a further critical impact on enhancing their ESG performance.

To verify the aforementioned hypothesis, sample enterprises were classified according to whether they implemented CEO duality. Empirical findings displayed in

Table 12. The Heterogeneity Analysis Results of CEO Duality.

Variable	(1) CEO Duality_High ESG	(2) CEO Duality_Low ESG
DID_Enterprise	1.4781 ** (1.82)	0.6369 (1.65)
Top1	−0.0075 (−0.62)	0.0095 * (1.80)
Board	0.1243 (1.54)	0.1208 *** (3.15)
Indpe	0.0648 *** (3.30)	0.0736 *** (6.71)
Lev	−0.0397 *** (−7.50)	−0.0213 *** (−6.69)
Fixed	−0.0170 ** (−2.28)	−0.0155 *** (−3.27)
Rota	1.6698 (1.24)	3.5902 *** (4.19)
Enterprise fixed effect	YES	YES
Year fixed effect	YES	YES
Obs	10,246	26,220
R2	0.6119	0.5619
Adj-R2	0.5150	0.5000
Fisher’s Permutation test	−0.841 *

Note: t statistics in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

indicate the DID_enterprise coefficient measures 1.4781 (5% significance) in the first column, declining to 0.637 in the second column. Moreover, Fisher’s Permutation Test for inter-group coefficient differences produced a significant outcome. This indicates that the enhancing impact of SCD on corporate ESG performance is more prominent among sample enterprises implementing CEO duality.

5.3.3. Market Attention and ESG Performance

Analysts’ research reports on enterprises exert a direct influence on investor decisions in the market. In order to attract investors, enterprises are impelled to improve their ESG performance so as to demonstrate their sustainability capabilities [85]. As crucial information intermediaries in capital markets, analysts’ corporate research reports have a significant impact on investor decisions. To obtain analyst attention, enterprises actively disclose information related to SCD progress, environmental impacts, and social responsibility fulfillment, thereby reducing information asymmetry and enhancing market recognition. Simultaneously, analyst attention and positive evaluations contribute to the enhancement of corporate reputation. During SCD initiatives, the strong corporate performance disseminated by analysts helps to cultivate a positive market image, strengthening brand value and competitiveness. This not only promotes long-term corporate development but also attracts ESG-focused investors and partners, forming a self-reinforcing cycle that further improves ESG performance. Therefore, it is hypothesized that SCD has a further important effect on improving ESG performance in enterprises with higher analyst attention compared to those with lower attention.

Sample enterprises were categorized according to the frequency of analyst research report attention that listed companies received within a year. An enterprise was classified as having relatively high analyst attention (An_high) if the frequency of its analyst attention was greater than the midpoint value for comparable industry participants in the given calendar year; otherwise, it was classified as having relatively low analyst attention (An_low). The test results, as presented in

Table 13. The Heterogeneity Analysis Results of Market Attention.

Variable	(1) An_High ESG	(2) An_Low ESG
DID_Enterprise	0.9701 ** (2.33)	−0.6915 (−1.07)
Top1	−0.0077 (−0.76)	0.0019 (0.27)
Board	0.0645 (1.16)	0.0871 (1.64)
Indpe	0.0874 *** (5.49)	0.0584 *** (4.14)
Lev	−0.0164 *** (−2.98)	−0.0271 *** (−6.32)
Fixed	−0.0168 ** (−2.05)	−0.0069 (−1.19)
Rota	−0.7492 (−0.46)	0.5329 (0.44)
Enterprise fixed effect	YES	YES
Year fixed effect	YES	YES
Obs	11,163	13,277
R2	0.5644	0.5855
Adj-R2	0.4677	0.4742
Fisher’s Permutation test	−1.662 ***

Note: t statistics in parentheses. ** p < 0.05, *** p < 0.01.

, demonstrate that the coefficient of DID_enterprise in column (1) is 0.9701 and statistically significant at the 5% level, whereas it is insignificant in column (2). Moreover, Fisher’s Permutation Test for inter-group coefficient differences produced a significant outcome. This suggests that the enhancing effect of SCD on corporate ESG performance is more prominent among sample enterprises with higher analyst attention, thereby validating our hypothesis.

6. Mechanism Test

The previous section offers empirical evidence for the notable improvement of corporate ESG performance through SCD. This section conducts a further exploration of the underlying mechanisms. Based on the earlier theoretical analysis, the main channels through which SCD enhances corporate ESG performance are alleviating financing constraints, promoting digital technology innovation, and enhancing the composition of human resources. Jiang notes that the Baron and Kenny stepwise method [86] for testing mediation has endogeneity problems caused by reverse causality. Accordingly, this study uses Jiang’s mediation analysis approach to test the causal effect of the independent variable on the mediator [87]. Theories and the literature are cited to support the effect of the mediator on the dependent variable, as explained in the text.

6.1. Mechanism Test from the Perspective of Financing Constraints

In terms of the measurement of financing constraints, this study refers to the approach of Xu et al. [88] and employs the absolute value of the SA Index as the proxy variable for financing constraints. Empirical evidence in

Table 14. Financing Constraints Mechanism Test Results.

Variable	SA
	(1)	(2)
DID_Enterprise	−0.0862 *** (−6.67)	−0.0005 *** (−2.88)
Top1		−0.0003 (−1.47)
Board		0.003 ** (2.41)
Indpe		−0.0001 (−0.54)
Lev		0.0004 *** (4.21)
Fixed		−0.0002 (−1.53)
Rota		0.019 (1.10)
Enterprise fixed effect	YES	YES
Year fixed effect	YES	YES
Obs	41,129	41,129
R2	0.9518	0.9516

Note: t statistics in parentheses. ** p < 0.05, *** p < 0.01.

indicates SCD demonstrates a statistically significant effect at the 1% level in alleviating corporate financing constraints. Through mechanisms such as enhancing information transparency, optimizing financing channels, and promoting the development of supply chain finance, SCD effectively mitigates the financing constraints faced by firms. Financing constraints are associated with firms’ environmental and social investment behavior [89]. Gao et al. pointed out, according to the cost of capital theory, firms use financing to support environmental, social responsibility, and SD projects. A high cost of finance increases the FC of firms, reduces available capital, and constrains environmental and social inputs. Lowering the cost of finance can help alleviate FC and increase firms’ ability to finance ESG investments and improve performance [12].This verifies that financing constraints play a significant mediating role in the process through which SCD promotes the enhancement of corporate ESG performance. Therefore, Hypothesis 2 is verified.

6.2. Mechanism Test from the Perspective of Digital Technology Innovation

Regarding the measurement of digital technology innovation, referring to the research of Li et al. [90], this research utilized annual counts of corporate digital invention patents, transformed via natural logarithms, to quantify technological innovation in digital domains. As shown in

Table 15. Digital Technology Innovation Mechanism Test Results.

Variable	Digital Technology Innovation
	(1)	(2)
DID_Enterprise	0.3276 ** (2.34)	0.3251 ** (2.30)
Top1		0.0014 (0.61)
Board		0.0257 ** (2.04)
Indpe		0.0002 (0.07)
Lev		0.0028 ** (2.47)
Fixed		−0.0007 (−0.45)
Rota		0.386 (1.60)
Enterprise fixed effect	YES	YES
Year fixed effect	YES	YES
Obs	19,906	19,906
R2	0.7308	0.7313

Note: t statistics in parentheses. ** p < 0.05.

, SCD can significantly promote corporate digital technology innovation at the 5% level. Wang and Yang’ research indicated that digital innovation can promote the use of clean energy and the development of renewable energy, facilitating the transition of companies to a low-carbon economy and improving their environmental performance [12]. Furthermore, Wang and Tang’ research found that both substantive and symbolic digital innovation can improve corporate ESG performance, but substantive digital innovation has a more significant contribution [91]. This verifies that digital technology innovation plays a critical mediating role in the process through which SCD promotes the enhancement of corporate ESG performance. Therefore, Hypothesis 3 is verified.

6.3. Mechanism Test from the Perspective of Human Capital Structure Optimization

Regarding the measurement of human capital structure optimization, Xiao et al. [92] developed corporate human capital structure indicators from two dimensions: occupational type and educational attainment. They gauged the skill sophistication of the human capital structure by utilizing the proportion of highly skilled employees, and evaluated the educational sophistication through the percentage of employees with master’s degrees or higher. The “living labor” indicator in the CSMAR database is computed by weighting the standardized values of three metrics: R&D personnel salary ratio, R&D personnel ratio, and highly educated personnel ratio, taking into account both skill sophistication and educational sophistication. Consequently, this study selects the “living labor” as the indicator for human capital structure measurement. The test results, as shown in

Table 16. Human Capital Structure Optimization Mechanism Test Results.

Variable	Human Capital Structure Optimization
	(1)	(2)
DID_Enterprise	0.0048 ** (2.45)	0.0044 ** (2.40)
Top1		0.0002 *** (5.75)
Board		0.0004 (1.46)
Indpe		0.00009 (1.51)
Lev		−0.0001 *** (−6.68)
Fixed		−0.0001 *** (−3.36)
Rota		0.0221 *** (4.69)
Enterprise fixed effect	YES	YES
Year fixed effect	YES	YES
Obs	27,662	27,662
R2	0.7003	0.7056

Note: t statistics in parentheses. ** p < 0.05, *** p < 0.01.

, indicate that the implementation of the Supply Chain Application and Innovation Pilot Policy can significantly optimize corporate human capital structure at the 5% level. SCD improves the human capital structure by aggregating high-skilled talents, promoting skill upgrading and training, and optimizing labor allocation. Fulmer and Ployhart pointed out that, human capital is the combination of skills, knowledge, ability and other attributes that can be transformed into productive forces, and plays a key role in the enterprise’s absorption and organizational knowledge innovation in the process of production [93]. The perspective of enterprise governance, Yu’s research indicated that human capital structure enhances the efficiency of enterprise resource allocation, and strengthens enterprises’ core competitiveness [81]. The perspective of environmental, He and Chen found that an optimized human capital structure plays a crucial role in enhancing ESG performance [80]. Skilled and knowledgeable employees are indispensable for the development and implementation of eco-friendly technologies, thus reducing environmental impact and ensuring compliance with environmental regulations [80]. This validates the mediating role of human capital structure optimization in the operation of SCD boosting corporate ESG performance, confirming Hypothesis 4.

7. Discussion

This study empirically validates that SCD exerts a positive influence on corporate ESG performance via pilot policies related to innovation and application in supply chain management, thus enriching the theoretical framework concerning their relationship. Drawing upon Jiang’s research [87], we conduct an analysis of the transmission mechanisms of financing constraints, digital technology innovation, and human capital structure, offering a novel perspective for comprehending the intricate interactions between SCD and corporate ESG performance. The heterogeneity analysis centers on three crucial dimensions from the perspective of corporate governance: board diversity, dual leadership roles, and market focus. These factors are closely correlated with both SCD initiatives and corporate ESG performance.

Specifically, prior research has concentrated on corporate digital transformation [81,94,95], indicating that such endeavors can elevate corporate ESG performance and contribute to sustainable development. Nevertheless, there exists a fundamental disparity between corporate digital transformation and SCD in the conceptual aspect. SCD emphasizes cross-enterprise collaboration, whereas corporate digitalization places emphasis on internal integration. This study extends the existing research by concentrating on cross-enterprise collaborative SCD. The benchmark regression results illustrate that SCD exerts a positive promotional influence on corporate ESG performance. The research underscores that as a crucial operational element, the supply chain reshapes its operation and management processes through digital construction, curtail resource waste and environmental pollution, enhance resource integration efficiency, and augment technological innovation capabilities. These improvements further facilitate corporate governance and the discharge of environmental and social responsibilities.

The heterogeneity analysis is an innovative aspect of this study. Tian et al. explored the transmission mechanism of external supervision in relation to SCD and corporate ESG performance [49]. Based on this research, it was discovered that in enterprises subject to stronger external supervision, SCD exerts a more substantial influence on corporate ESG performance. Considering that board governance and management governance play a crucial role in corporate decision-making and have significant impacts on policy implementation and ESG performance, this study conducts a heterogeneity analysis of board diversity and dual CEO within the realm of corporate governance. It was demonstrated that in enterprises characterized by more diversified boards and dual CEO structures, SCD has a more important effect on corporate ESG performance.

In the mechanism analysis, this study uncovers that SCD enhances corporate ESG performance via three channels: alleviating financing constraints, strengthening digital technology innovation, and optimizing the human capital structure. These findings are in line with prior research [65,81,96,97,98]. The analysis indicates that financing constraints act as a crucial transmission mechanism connecting SCD to corporate ESG performance, which aligns with the conclusions drawn by Zhu and Zhang [99]. While existing studies predominantly emphasize the transmission role of green innovation capabilities [48], this study employs digital technology innovation as an intermediary variable, recognizing it as the technological achievement under SCD. The research reveals that SCD propels enterprises towards transparent, lean, and intelligent operations through digital innovation, ultimately attaining sustainable development objectives. While prior research has analyzed the transmission mechanism of human capital structure optimization between corporate digital transformation and environmental, social, and governance results [80,81], this research extends the analysis by investigating the transmission mechanism of human capital structure optimization between SCD and corporate ESG performance, thereby deepening the understanding of the linkage mechanisms underpinning SCD and corporate ESG performance. The results demonstrate that SCD reshapes enterprise operations, management, and production models, intensifies the skill-biased nature of human capital demand, optimizes human capital structure, further refines production processes and resource allocation, and enhances the scientific nature and effectiveness of corporate decision-making, thereby elevating corporate ESG performance.

8. Conclusions and Policy Recommendations

This study employs the difference-in-differences (DID) model to analyze Chinese A-share listed companies during the period from 2009 to 2023. By taking advantage of supply chain application and innovation pilot policies, this research investigates the influence of SCD on corporate ESG performance and explores its potential transmission mechanisms. Multiple robustness tests are applied to verify these structural relationships. The research results demonstrate that SCD not only directly improves corporate ESG performance but also indirectly promotes it via mediating mechanisms such as alleviating financing constraints, spurring digital technology innovation, and optimizing human capital structures. Heterogeneity analysis reveals considerable disparities in the influence of SCD on ESG performance among enterprises with varying degrees of external supervision, board structures, and CEO duality. Specifically, enterprises featuring stronger external supervision, more diversified boards, and CEO duality show more notable enhancements in ESG performance through digitalization.

Drawing upon empirical evidence, we propose the following policy recommendations. Policymakers should promote SCD within industrial clusters and encourage firms to prioritize it as a core strategic initiative. Furthermore, fiscal and tax incentive policies could be implemented to support supply chain finance platforms, improve capital allocation channels, and alleviate corporate financing pressures. Corporate managers may consider adopting a leadership structure where the chairman concurrently serves as the CEO to enhance strategic execution efficiency and resource integration capabilities, thereby facilitating the achievement of ESG objectives. However, this approach should be complemented by a diverse and professional board composition to mitigate potential risks. Additionally, enterprises should optimize their human capital structure by providing targeted professional training and actively recruiting interdisciplinary talents proficient in both digital skills and sustainable development principles. From a deeper perspective, China’s economic development follows a “policy guidance + market-driven” two-wheel model, while most other economies in the world rely more on a “market force + industry self-regulation” model. Hence, large multinational corporations should take a leading role by leveraging their central position to require and support their global suppliers in undergoing supply chain digital transformation. Moreover, International Organization of Standardization(ISO) ought to establish global-recognized standards for SCD and ESG disclosure to reduce compliance complexities for businesses worldwide.

In summary, this study provides valuable insights for policymakers and enterprise managers, encouraging their active participation in building SCD to promote the intelligence and sustainable development of the industry. However, this research still has some limitations. On the one hand, although the study has passed robustness tests such as PSM-DID and placebo tests, it cannot fully eliminate endogeneity issues arising from reverse causality. Future research could consider employing an instrumental variable approach to address this problem. On the other hand, this study focuses primarily on China’s A-share listed companies, and the generalizability of its conclusions may be constrained by factors such as market environment and institutional context. Subsequent research could extend to samples from other countries and regions, particularly comparative analyses between emerging and mature markets, to explore the differences and underlying mechanisms in the impact of SCD on corporate ESG performance under different institutional environments. Furthermore, the specific pathways through which SCD influences corporate ESG performance by optimizing human capital structure—such as the heterogeneous roles of different types of human capital (e.g., technical, managerial, environmental)—and the moderating effects of informal institutional factors like corporate culture and organizational structure during digitalization remain to be further investigated. Expanding research in these directions will contribute to a more comprehensive and in-depth understanding of the complex relationship between SCD and corporate sustainable development, providing more targeted theoretical insights and practical implications for the greening and digitalization of global supply chains.