The Impact of Corporate ESG Performance on Supply Chain Resilience: A Mediation Analysis Based on New Quality Productive Forces

Abstract

In light of the frequent occurrence of uncertain events, supply chain resilience has emerged as a critical issue for the survival and development of enterprises. This study empirically examines the impact of corporate environmental, social, and governance (ESG) performance on supply chain resilience, utilizing data from A-share listed companies in China from 2015 to 2023. The findings reveal that strong ESG performance positively influences supply chain resilience. The concept of “new quality productive forces” provides a novel perspective for understanding corporate sustainable development. Mechanism tests indicate that new quality productive forces play a significant mediating role between ESG performance and supply chain resilience. Specifically, by enhancing ESG performance, enterprises indirectly promote the growth of new quality productive forces, thereby further strengthening supply chain resilience. The robustness of these results is confirmed through tests involving the replacement of core explanatory variables, expansion of sample size, inclusion of additional control variables, and Hausman Tests. Furthermore, heterogeneity analysis demonstrates that state-owned enterprises exhibit a more pronounced effect of ESG performance on supply chain resilience compared to private enterprises.

1. Introduction

Recent years have seen escalating vulnerabilities in corporate supply chains due to evolving global energy dynamics, recurrent geopolitical conflicts, and public health crises. The penetration rate of e-commerce has risen significantly worldwide in the post-pandemic era, with consumer behavior rapidly shifting toward online shopping. This trend has driven a structural reconstitution of logistics networks from centralized to distributed models, imposing heightened demands on supply chain flexibility and digital capabilities. For instance, studies in Poland demonstrate that the scale of e-commerce increased by over 300% between 2010 and 2020, a trend further accelerated by the pandemic, which has directly impacted resource allocation and operational efficiency in the transportation sector [1]. This transformation underscores the dual challenges faced by supply chains in balancing efficiency and resilience while adapting to emerging consumption patterns. Enhancing supply chain resilience to mitigate external risks has thus become a critical imperative for corporate survival and development. The 20th National Congress of the Communist Party of China underscored the importance of “strengthening the resilience and security of industrial and supply chains”. Supply chain resilience, defined as an enterprise’s ability to adapt flexibly and respond rapidly to risks and disruptions, is now central to strategic decision-making. Amid profound shifts in the global economic landscape and the accelerating integration of sustainable development principles, environmental, social, and governance (ESG) practices have evolved from investment criteria to a strategic tool for reshaping business ecosystems. ESG performance not only reflects a firm’s capabilities in environmental stewardship, social responsibility, and governance but also serves as a cornerstone for building long-term competitive advantages. The concept of new quality productive forces represents a productivity paradigm characterized by new technologies, new economic forms, and new business models [2]. Within the framework of Marxist political economy, this study defines it as “a productivity formation with technological innovation as its core driver, deeply integrated with green transformation, digitalization, and networking features”. Its fundamental distinctions from traditional productive forces manifest in three key dimensions [3,4]:

1.

Factor Structure: Data and intellectual capital supplant traditional inputs as dominant drivers.

2.

Operational Mechanisms: Disruptive innovations in production processes emerge through advanced technologies such as digital systems and generative artificial intelligence.

3.

Value Orientation: A dual emphasis on economic efficiency and ecological sustainability replaces singular profit maximization.

Rooted in the classical paradigm of productivity shaping production relations, new quality productive forces align with China’s high-quality development goals, offering a novel theoretical lens to analyze how ESG performance enhances supply chain resilience.

Current studies demonstrate that corporate ESG performance enhances supply chain resilience through mechanisms such as reducing information asymmetry, alleviating financing constraints, and promoting R&D investments, with resilience often measured from the perspective of supply–demand relationships [5]. Additional research identifies the reduction of analyst forecast dispersion as a critical pathway through which ESG performance influences supply chain concentration [6]. However, significant gaps persist in the literature:

1.

Overemphasis on Traditional Resource Allocation: Existing mediation mechanisms predominantly focus on the allocation of conventional production factors (e.g., capital, labor), neglecting the mediating role of new quality productive forces—a paradigm driven by technological innovation and characterized by green, digital, and networked features.

2.

Static Evaluation Frameworks: Current assessments of supply chain resilience rely heavily on static structural indicators, failing to adequately capture dynamic processual dimensions such as preventive, adaptive, and restorative capabilities.

From a practical standpoint, China is accelerating the construction of a new development paradigm and promoting the optimization and upgrading of industrial and supply chains, explicitly requiring enterprises to leverage ESG principles to achieve high-quality development. Therefore, clarifying the mechanisms through which ESG performance influences supply chain resilience is not only a theoretical necessity but also a practical imperative for policy formulation and corporate strategic adjustments. The concept of “new quality productive forces” offers a novel perspective for understanding corporate sustainable development, but its role as a bridge between ESG and supply chain resilience remains underverified.

Drawing upon data from China’s A-share listed companies (2015–2023) and grounded in dynamic capability theory, this study deconstructs supply chain resilience into three processual dimensions: preventive, adaptive, and restorative capacities. It systematically analyzes the pathways through which ESG performance influences supply chain resilience and innovatively introduces new quality productive forces as a mediating variable to explore its transmission mechanisms. The contributions of this study are threefold: First, it expands the application boundaries of ESG theory by revealing the intrinsic logic through which ESG performance enhances supply chain resilience via multiple pathways. Second, it constructs a mediating model of new quality productive forces between ESG and supply chain resilience, providing a new perspective for understanding the synergistic mechanisms of sustainable development and supply chain management. Third, through heterogeneity analysis based on property rights, it highlights the policy advantages of state-owned enterprises in leveraging ESG to enhance supply chain resilience, offering empirical evidence for differentiated policy design.

The subsequent structure of this paper is organized as follows: The second section reviews the literature on ESG performance and supply chain resilience, identifying gaps in existing research. The third section proposes research hypotheses based on dynamic capability theory and social exchange theory. The fourth section designs the empirical model and describes the data sources. The fifth section presents the baseline regression results, robustness tests, endogeneity tests, mediation effect analysis, and heterogeneity analysis. The sixth section summarizes the findings and provides policy recommendations.

2. Literature Review

2.1. Research on Corporate ESG Performance

The concept of ESG (environmental, social, and governance) was first introduced in the 2004 report Who Cares Wins as a comprehensive evaluation framework encompassing three dimensions: environmental, social, and governance. It serves as a tool to assess a company’s capabilities in addressing environmental challenges, fulfilling social responsibilities, and optimizing internal governance. In recent years, scholars have reinterpreted the ESG concept in light of emerging trends. Pollman (2024) [7] analyzed the diverse applications of ESG in investment analysis, risk management, corporate social responsibility, and sustainability, highlighting its centrality in global investment and corporate governance. Xiao (2024) [8] redefined ESG from the perspectives of subject and essence, describing it as “the organization’s effective management of the environmental and social impacts of its decisions and activities, as well as the influence of environmental, social, and governance factors on organizational operations, with the aim of maximizing contributions to sustainable development and achieving organizational sustainability in terms of willingness, behavior, and performance”. Gao (2024) [9] proposed a new ESG development framework under the lens of “new quality productive forces”, advocating for a shift from an evaluation-oriented perspective to an action-oriented one, from a balance-oriented perspective to a momentum-oriented one, and from an individual-oriented perspective to an ecosystem-oriented one. As the ESG concept continues to evolve, the development of various ESG evaluation systems has become a focal point of research, aiming to provide actionable decision-making tools for enterprises, investors, and policymakers. For assessing the ESG performance of listed companies, scholars primarily rely on ESG ratings disclosed by third-party institutions. Currently, mainstream ESG rating indices in academia are published by institutions such as Hua Zheng, Wind, Bloomberg, SynTao Green Finance, and CNRDS.

Academic discussions on ESG largely follow a cause-and-effect research framework. Studies focusing on the drivers of ESG in the context of corporate management and strategy primarily emphasize economic factors, legal and policy factors, and investors’ social responsibility philosophies (Daugaard, 2022) [10]. Mu (2023) [11] found a positive relationship between digital finance and corporate ESG performance, noting that digital finance alleviates financial constraints, thereby encouraging greater ESG engagement. Fang (2023) [12] demonstrated that corporate digitalization enhances ESG ratings, particularly in the governance (G) and social (S) dimensions. Wang (2023) [13] explored the direct negative impact of environmental uncertainty on corporate ESG performance, validating this relationship through both “emotional” (investor sentiment) and “rational” (green innovation) perspectives. That study also found that the negative effects of environmental uncertainty are more pronounced in mature enterprises and markets with strong government intervention. Li (2024) [14] examined the impact of regional digitalization on corporate ESG performance within the context of China’s digital economy innovation pilot zones, revealing that the effects of digital policies are more significant in regions with high environmental awareness and among enterprises with environmentally conscious senior management. Jang (2022) [15] investigated how executive incentives influence corporate ESG performance, finding that short-term executive incentives conflict with ESG goals, leading to poorer ESG performance. As a critical driver of sustainable development, the economic consequences of ESG can be categorized into its internal operational impacts and external market effects (Chen 2024 [16]; Li, T.-T, 2021 [17]). Lu (2024) [18] found that corporate ESG ratings significantly promote low-carbon investment decisions in renewable energy enterprises, generating regional spillover effects. Chen (2023) [19] explored the relationship between ESG performance and corporate financial performance, revealing a significant positive correlation, particularly for large-scale enterprises and high-risk companies. Zhang H (2024) [20] found that corporate ESG performance positively impacts green innovation through resource effects, governance effects, and innovation effects.

Rahman (2023) [21] demonstrated that ESG (both composite and individual dimensions) positively influences corporate performance, highlighting the moderating roles of sustainable development strategies and senior management commitment. Cheng (2025) [22] found that corporate ESG performance adversely affects labor share, suggesting that enterprises should adopt a more balanced approach in pursuing sustainable development.

Existing research has examined corporate ESG performance from conceptual, motivational, and consequential perspectives, yet three critical limitations persist: First, the research perspective exhibits a fragmented nature. Prior studies either focus on isolated dimensions of ESG or analyze its economic consequences in a compartmentalized manner, failing to systematically elucidate the dynamic mechanisms through which ESG’s multidimensional synergies influence complex supply chain systems. Second, investigations into mediation mechanisms remain confined to traditional production factor frameworks, with scant attention paid to new quality productive forces as a mediating channel. The enabling pathways through which ESG enhances resilience thus retain a “black box” characteristic. These gaps underscore the necessity of constructing a dynamic “ESG–new quality productive forces–supply chain resilience” framework.

2.2. Research on Supply Chain Resilience

The concept of supply chain resilience emerged in the early 21st century as a response to the vulnerabilities exposed by accelerated globalization. Christopher (2004) [23] defined it as “the ability of a supply chain system to recover to its original or an improved state within an acceptable time frame after a disruption”. Existing research primarily focuses on three core themes: the construction of resilience evaluation systems, the analysis of influencing factors, and the exploration of dynamic enhancement pathways.

In terms of evaluating supply chain resilience, the current literature mainly employs three methods to quantify it (Ivanov, 2025) [24]. The first method involves indirect measurement through supply chain performance indicators. Patidar (2023) [25] identified 16 relevant indicators, including lead time and order cycle time, and analyzed these metrics using the fuzzy analytic hierarchy process (AHP) based on expert opinions. Similarly, Das (2022) [26] combined AHP with the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to analyze 11 indicators, such as process automation, artificial intelligence, and inventory management. The second method utilizes graph theory for indirect measurement. Agarwal (2022) [27] applied graph theory to quantify supply chain resilience as a single numerical value, termed the resilience index. Comparing a company’s resilience index with industry benchmarks aids in strategic planning. López-Castro (2021) [28] emphasized that sustainability goals and supply chain resilience should be integrated as key criteria in supply chain network design. The third method involves direct measurement using specific resilience indicators. Hosseini (2016) [29] employed a Bayesian network model to quantify the absorptive, adaptive, and restorative capacities of supply chain resilience. Moosavi (2021) [30] used discrete event simulation models to simulate various resilience strategies, calculating resilience based on the quantity of items not received by the recipient in the absence of recovery plans.

Regarding the factors influencing supply chain resilience, internal structural factors play a significant role. Zhao (2023) [31] found that supply chain digitalization differentially impacts the three dimensions of supply chain resilience—absorption, adaptation, and recovery—through the reconfiguration of resources and structures. From an external market perspective, Dubey (2023) [32] demonstrated that government effectiveness enhances supply chain resilience by improving digital adaptability and agility.

In terms of dynamic enhancement pathways for supply chain resilience, technologies associated with Industry 4.0, such as artificial intelligence and big data analytics, have shown significant potential in enhancing resilience (Spieske, 2021 [33]; Zamani, 2023 [34]; Modgil, 2022 [35]). Yin (2024) [36] applied fuzzy-set qualitative comparative analysis (fsQCA) to identify three pathways for improving supply chain resilience: capability-driven, relationship-driven, and capability–relationship matching.

Existing research has laid important theoretical foundations for assessing and enhancing supply chain resilience, yet a theory–practice gap persists, manifested in three key limitations:

1.

Measurement System Dilemma: Current approaches are trapped in a “structure–performance” dichotomy. While structural indicators facilitate quantification, they fail to capture the dynamic “prevention–adaptation–recovery” continuum, whereas simulation-based performance metrics lack cross-context comparability, resulting in deviations between resilience assessments and actual risk resistance capabilities.

2.

Technological Determinism Bias: Studies emphasize singular pathways like digital technologies or government effectiveness, neglecting to integrate the institutional embeddedness of ESG practices with technological adaptability. This oversight creates a partial understanding of resilience drivers.

3.

Fragmented ESG–Supply Chain Linkages: Research either narrowly examines unilateral supplier management effects (Chen 2023) [37] or focuses solely on power restructuring (Li 2023) [38], failing to establish a systematic mediation model with new quality productive forces as the conduit. These theoretical gaps provide the entry point for our mediation model.

3. Theoretical Analysis and Research Hypotheses

3.1. The Direct Impact of Corporate ESG Performance on Supply Chain Resilience

Supply chain resilience refers to the ability of a supply chain system to maintain stability, adapt to changes, and recover to its original or an improved state when faced with external environmental changes or disruptions. Under the framework of dynamic capability theory, supply chain resilience can be deconstructed into three dimensions: preventive capability, adaptive capability, and restorative capability.

Preventive capability refers to a firm’s ability to reduce or avoid supply chain disruptions through effective risk identification and mitigation measures when facing potential risks. Companies focused on ESG practices are generally better prepared for potential disruptions and can effectively mitigate risks (Singh, C. S., 2019) [39]. Specific measures include selecting reliable suppliers and enhancing production flexibility (ur Rehman A., 2022) [40]. By optimizing production processes, improving energy efficiency, and reducing pollution emissions, companies can lower external environmental risks and reduce the likelihood of supply chain disruptions caused by environmental factors. Additionally, fulfilling social responsibilities enhances corporate reputation, enabling firms to gain more social support and investment. Strong governance structures improve the efficiency and transparency of supply chain decision-making, strengthen supplier management, and diversify supplier channels, thereby reducing risks associated with supply chain concentration (Letizia, P., 2016) [41].

Adaptive capability refers to the ability of a supply chain to maintain normal operations when confronted with sudden disruptions or external environmental changes. According to social exchange theory, companies that establish robust social responsibility systems and strengthen relational communication and reciprocal commitments can build strong cooperative relationships with supply chain members (Saglam Y. C., 2022) [42]. This enhances the flexibility and diversified configuration of the supply chain. When disruptions occur, companies with strong ESG performance demonstrate a strong ability to withstand risks (Tsang Y. P., 2024) [43]. Such firms can quickly adjust the allocation of supply chain resources to maintain operational stability. Stakeholder theory further suggests that strong ESG performance helps companies strengthen relationships with employees, communities, and consumers. In the face of sudden social events, collaborative efforts can enhance the supply chain’s ability to resist risks.

Restorative capability refers to the ability of a supply chain to quickly recover to its original or an improved state after experiencing severe shocks. Strong ESG performance can improve supply chain efficiency (Yang F., 2024) [44]. When a supply chain disruption occurs, the efficiency of corporate governance enables the supply chain to make optimal decisions and implement them promptly. In times of geopolitical instability, government agencies mandating ESG disclosures and incentivizing sustainable development can help companies reduce external risks, improve financial resilience, and attract stable investments (Reyad H. M., 2024) [45], thereby enhancing the supply chain’s restorative capability.

Based on the above analysis, this study proposes the following hypothesis:

H1: 

Strong corporate ESG performance enhances the resilience level of the supply chain.

3.2. The Mediating Role of New Quality Productive Forces

New quality productive forces constitute one of the core factors driving corporate sustainable development. Unlike traditional productivity, they integrate digital technological innovation, green development, and social responsibility practices to enhance the overall efficiency and adaptability of supply chains [46]. New quality labor, new quality labor objects, and new quality labor materials all play unique roles in improving supply chain resilience.

In the environmental dimension, corporate investments in reducing carbon emissions, improving energy efficiency, and enhancing the utilization of green resources contribute to the development and application of green production technologies and clean technologies [47]. These efforts enable more efficient production methods, reduce supply chain input costs, improve output efficiency, and advance sustainable development strategies. In the social dimension, companies that strengthen employee welfare measures, fulfill social responsibilities, and promote collaboration with stakeholders are better positioned to build production models with greater social benefits. This accumulation of social capital further drives the enhancement of new quality productive forces. In the governance dimension, a robust corporate governance structure ensures higher management efficiency and decision-making transparency. When facing uncertain and complex disruptions, such governance enables supply chains to respond quickly and optimize resource allocation, thereby enhancing overall new quality productive forces.

New quality labor possesses the ability to predict risks and make forward-looking decisions. Through digital technologies, the real-time monitoring and analysis of supply chains improve visibility and traceability. Transparent supply chain networks allow for the early identification of potential risks, enabling proactive preventive measures to avoid disruptions. Additionally, real-time data and market changes facilitate rapid adjustments to supply chain strategies. Digital platforms and tools such as blockchain enable efficient information sharing and collaboration within supply chain networks, helping companies respond swiftly to changes in demand. In the event of a supply chain disruption, technology and data analytics can quickly identify and prioritize the recovery of critical components, providing efficient restoration support [48].

Based on the above analysis, this study proposes the following hypothesis:

H2: 

Strong corporate ESG performance promotes the enhancement of new quality productive forces, thereby improving the level of supply chain resilience.

4. Research Design

4.1. Variable Description

4.1.1. Dependent Variable: Supply Chain Resilience (Resilience)

This study adopted supply chain resilience as the dependent variable and measured it across three process dimensions: preventive capability, adaptive capability, and restorative capability (Pettit, 2010) [49]. Drawing on the research of Song Donglin (2024) [50] and Zhang Shushan (2023) [51], this paper incorporated perspectives from both customers and suppliers. Data on the fund utilization, top five customers, and supplier relationships of listed companies were collected, and the entropy weight method was applied to calculate the final results. The evaluation system for corporate supply chain resilience is presented in

Table 1. Corporate Supply Chain Resilience Evaluation System.

Target Layer	First-Level Indicators	Second-Level Indicators	Indicator Description
Supply Chain Resilience	Preventive Capability	Fund Occupancy	Natural logarithm of the ratio of accounts receivable to revenue
		Customer Concentration	Ratio of sales to the top five customers to total annual sales
		Supplier Concentration	Ratio of purchases from the top five suppliers to total annual purchases
	Adaptive Capability	Customer Stability	Number of overlapping top five customers compared to the previous year, divided by five
		Supplier Stability	Number of overlapping top five suppliers compared to the previous year, divided by five
	Restorative Capability	Product Flow	Inventory turnover ratio of the enterprise
		Performance Volatility	Sensitivity index of economic performance

.

The supply chain resilience indicator system in this study overcomes the static limitations of traditional structural metrics by dynamically tracking performance across the three stages of prevention, adaptation, and recovery, thereby comprehensively reflecting the evolutionary process of resilience. In terms of preventive capacity, the capital occupancy situation reflects a company’s management level of capital liquidity within the supply chain. By monitoring capital liquidity dynamics, it identifies potential cash flow disruption risks caused by delayed customer payments, providing a basis for preventive financial reserves. Both customer concentration and supplier concentration reflect the structural risks and risk buffering capacity of the supply chain network. Regarding adaptive capacity, customer stability and supplier stability reflect the sustainability of supply chain relationships, where high retention rates indicate the establishment of trust mechanisms through long-term cooperation. For restorative capacity, product flow (inventory turnover rate) serves as a key indicator measuring supply chain efficiency and recovery speed. Companies with high turnover rates can more quickly digest excess inventory or replenish shortages, thereby accelerating supply chain restoration. Performance volatility (economic performance sensitivity index) gauges recovery effectiveness by measuring the sensitivity of financial performance to supply chain shocks.

4.1.2. Independent Variable: Corporate ESG Performance (ESG)

This study used corporate ESG performance as the independent variable, measured by the Hua Zheng ESG composite score. To standardize the data, the ESG composite scores of listed companies were divided by 100, resulting in a normalized dataset with values ranging between [0, 1].

4.1.3. Mechanism Variable: New Quality Productive Forces (NQPFs)

The mechanism variable in this study was new quality productive forces. While the academic community has yet to establish a unified measurement system, the mainstream approach involves constructing an evaluation index system. This paper utilized the new quality productive forces research database from the CSMAR database, which references the work of Song Jia et al. (2024) to develop a three-level indicator table encompassing labor and production tools [52].

4.1.4. Control Variables

To mitigate the influence of related variables on supply chain resilience and to more accurately examine the impact of corporate ESG performance on supply chain resilience, this study selected the following control variables based on prior research on factors influencing supply chain resilience:

• Firm Size (Size): Natural logarithm of total assets, where “total assets” is measured in billions of yuan (CNY).
• Listing Age (ListAge): Natural logarithm of the number of years since the company’s initial public offering (IPO).
• Return on Assets (ROA): Ratio of total profit to total assets.
• Asset–Liability Ratio (Leverage): Ratio of total liabilities to total assets.
• Top One Shareholder Ownership (Top1): Proportion of shares held by the largest shareholder relative to the company’s total shares.
• Firm Growth (Growth): Year-on-year growth rate of operating revenue.
• Tobin’s Q (TobinQ): (Market value of tradable shares + Number of non-tradable shares × Net asset value per share + Book value of liabilities)/Total assets.
• Cash Flow Ratio (Cashflow): Net cash flow divided by total assets.

These control variables were included to account for potential confounding effects and to ensure the robustness of the analysis. By controlling for these factors, this study aimed to isolate the specific impact of ESG performance on supply chain resilience.

4.2. Data Sources and Descriptive Statistics

This study utilized a sample of Chinese A-share listed companies from 2015 to 2023 to investigate the impact of corporate ESG performance on supply chain resilience. After excluding companies with delisting risks (ST and *ST firms), financial industry firms, and firms with missing data, the final dataset comprised 5607 firm-year observations in a balanced panel. To mitigate the influence of outliers, all continuous variables were winsorized at the 1st and 99th percentiles.

The data on listed companies were sourced from the CSMAR and CNRDS databases, while corporate ESG performance data were obtained from Shanghai Hua Zheng Index Information Service Co., Ltd., Shanghai, China.

The descriptive statistics of the sample are presented in

Table 2. Descriptive Statistics of Variables.

Variable	N	Mean	SD	Min	Max
Supply Chain Resilience	5607.00	0.48	0.12	0.11	0.86
ESG	5607.00	0.73	0.05	0.59	0.84
NQPF	5607.00	0.01	0.01	0.00	0.05
Size	5607.00	22.56	1.09	20.34	25.76
Lev	5607.00	0.42	0.18	0.07	0.83
ROA	5607.00	0.04	0.06	−0.17	0.21
Cashflow	5607.00	0.05	0.06	−0.12	0.23
Growth	5607.00	0.13	0.31	−0.49	1.86
Top1	5607.00	0.31	0.14	0.08	0.71
TobinQ	5607.00	1.93	1.15	0.00	6.80
ListAge1	5607.00	2.57	0.46	1.39	3.37

. The mean value of supply chain resilience was 0.48, with a median of 0.49, a maximum value of 0.86, and a minimum value of 0.11. The significant gap between the maximum and minimum values reflects substantial variability in supply chain resilience across the sample firms. The mean value of ESG performance was 0.73, with a median of 0.74 and a standard deviation of 0.05. This indicates that the overall ESG performance of the sample firms was relatively strong, though some variability existed. The mean being slightly higher than the median suggests that most firms exhibited high ESG performance, while a small number of firms had lower ESG scores. The maximum value of ESG performance was 0.84, and the minimum value was 0.59, indicating a relatively narrow range of ESG performance across firms. The mean value of new quality productive forces was 0.01, with a median of 0.01 and a standard deviation of 0.01. The maximum value was 0.05, and the minimum value was 0.00, indicating that only a few firms had made significant breakthroughs in new quality productive forces, while the majority had limited investment and output in this area. The results for other control variables were consistent with findings in the existing literature, confirming the reliability and comparability of the dataset.

4.3. Model Specification

4.3.1. Baseline Regression Model

To examine whether corporate ESG performance has a positive impact on supply chain resilience, this study employed a fixed-effects model. The baseline regression model is specified as follows:

$${\mathrm{R}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{l}}_{\mathrm{i}\mathrm{t}}={\mathrm{\alpha }}_0+{\mathrm{\alpha }}_1{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\alpha }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{t}}+{\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(1)

In the model, ${\mathrm{R}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$ represents the supply chain resilience level of firm i in period t. ${\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i}\mathrm{t}}$ represents the ESG performance level of firm i in period t. ${\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}$ represents all control variables. ${\mathrm{\alpha }}_0,{\mathrm{\alpha }}_1,\mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{\alpha }}_2$ are parameters to be estimated. Specifically, ${\mathrm{\alpha }}_1$ captures the impact of corporate ESG performance on supply chain resilience. If the regression coefficient ${\mathrm{\alpha }}_1$ is positive and statistically significant, it supports Hypothesis H1. Furthermore, ${\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{t}}$ represents year fixed effects, controlling for time-specific shocks, while ${\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}}_{\mathrm{i}}$ represents industry fixed effects. Industry classification followed the China Securities Regulatory Commission (CSRC) 2012 industry standards. For manufacturing industries (with “C” codes), the first two digits were retained, while for other industries, only the first digit was retained. ${\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$ represents the random error term.

4.3.2. Mediation Model

To examine the mediating role of new quality productive forces, this study adopted the stepwise regression approach proposed by Wen (2014) [53]. The mediation model is specified as follows:

The first step involves estimating the direct effect of corporate ESG performance on supply chain resilience using the following regression model:

$${\mathrm{R}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{l}}_{\mathrm{i}\mathrm{t}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\beta }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{t}}+{\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(2)

Next, the impact of corporate ESG performance on new quality productive forces is examined:

$${\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}={\mathrm{\gamma }}_0+{\mathrm{\gamma }}_1{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\gamma }}_2{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{t}}+{\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(3)

${\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}$ represents the level of new quality productive forces of firm i in period t.

Finally, the mediating role of new quality productive forces is examined:

$${\mathrm{R}\mathrm{e}\mathrm{s}\mathrm{i}\mathrm{l}}_{\mathrm{i}\mathrm{t}}={\mathrm{\phi }}_0+{\mathrm{\phi }}_1{\mathrm{E}\mathrm{S}\mathrm{G}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\phi }}_2{{\mathrm{N}\mathrm{Q}\mathrm{P}\mathrm{F}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\phi }}_3\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i}\mathrm{t}}+{\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{t}}+{\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{u}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(4)

5. Empirical Results and Analysis

5.1. Baseline Regression Results

Before conducting the regression analysis, this study tested for multicollinearity to ensure the reliability of the regression results. The test results showed that the variance inflation factor (VIF) values for all variables were no greater than five, indicating no significant multicollinearity among the variables.

Table 3. Baseline Regression Results.

Variable	(1)	(2)	(3)
	Resil	Resil	Resil
ESG	0.229 *** (0.031)	0.235 *** (0.031)	0.153 *** (0.033)
Size			0.006 *** (0.002)
ListAge			−0.029 *** (0.004)
ROA			0.085 ** (0.036)
Leverage			−0.017 (0.012)
Top1			−0.006 (0.012)
Growth			0.038 *** (0.006)
TobinQ			0.003 * (0.002)
Cashflow			0.112 *** (0.029)
Cons	0.312 *** (0.023)	0.293 *** (0.026)	0.265 *** (0.043)
Controls	NO	NO	YES
Year	NO	YES	YES
Industry	NO	YES	YES
N	5607	5607	5607
R2	0.009	0.084	0.116

Note: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively; standard errors are reported in parentheses.

reports the impact of corporate ESG performance on supply chain resilience. The regression analysis was conducted using three different models:

• Column (1): Presents the regression results without control variables and fixed effects.
• Column (2): Includes industry and year fixed effects.
• Column (3): Further incorporates multiple control variables.

It can be observed that the R² value increases across the columns, indicating that the explanatory power of the model gradually improves. The results show that the regression coefficients in all three models are statistically significant at the 1% level, suggesting that strong corporate ESG performance significantly enhances supply chain resilience. These findings support Hypothesis 1 of this study.

5.2. Robustness Tests

5.2.1. Replacing the Explanatory Variable

Following the approach of Gao Jieying (2021) [54], this study replaced the explanatory variable with a new data format of the Hua Zheng ESG rating. The Hua Zheng ESG rating ranges from poor to excellent as follows: C, CC, CCC, B, BB, BBB, A, AA, AAA, and these nine levels were assigned values from one to nine, respectively. The hypotheses were retested using this alternative measure. The results in Column (1) of

Table 4. Robustness Tests.

Variable	Resilience
	(1) Replacing the Explanatory Variable	(2) Expanding the Sample Size	(3) Adding Control Variables
ESG	-	0.163 *** (0.031)	0.146 *** (0.033)
ESG (new)	0.007 *** (0.002)	-	-
Size	0.007 *** (0.002)	0.006 *** (0.002)	0.007 *** (0.002)
ListAge	−0.029 *** (0.004)	−0.022 *** (0.004)	−0.029 *** (0.004)
ROA	0.087 ** (0.036)	0.075 ** (0.034)	0.093 ** (0.036)
Leverage	−0.018 (0.012)	−0.021 * (0.011)	−0.018 (0.012)
Top1	−0.006 (0.012)	−0.007 (0.011)	−0.008 (0.012)
Growth	0.038 *** (0.006)	0.034 *** (0.005)	0.038 *** (0.006)
TobinQ	0.003 * (0.002)	0.004 ** (0.001)	0.003 (0.002)
Cashflow	0.112 *** (0.029)	0.125 *** (0.026)	0.113 *** (0.029)
Board	-	-	−0.006 (0.009)
Indep	-	-	0.074 ** (0.030)
Cons	0.344 *** (0.042)	0.217 *** (0.039)	0.252 *** (0.048)
Controls	YES	YES	YES
Year	YES	YES	YES
Industry	YES	YES	YES
N	5607	6853	5607
R2	0.116	0.125	0.117

Note: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively; standard errors are reported in parentheses.

are consistent with those in Table 3 in terms of coefficient signs and significance levels, further confirming the robustness of the findings.

5.2.2. Expanding the Sample Size

The sample period was extended to 2013–2023, increasing the sample size. The results in Column (2) of

Table 5. Two-Stage Least Squares (2SLS) Regression Results.

Variable	First-Stage	Second-Stage
ESG		0.169 *** (0.056)
L1.ESG	0.615 *** (0.016)
L2.ESG	0.031 ** (0.014)
Cons	0.119 *** (0.016)	0.260 *** (0.049)
Controls	YES	YES
Year	YES	YES
Industry	YES	YES
N	4361	4361
First-Stage F-statistic	1282.56
Kleibergen–Paap LM Test	933.56 ***
Cragg–Donald Wald F-statistic	1623.90
Anderson–Rubin Wald Test	5.41 ***

Note: **, and *** indicate statistical significance at the 5%, and 1% levels, respectively; standard errors are reported in parentheses.

show that the direction and significance of the impact of corporate ESG performance on supply chain resilience remained unchanged, indicating that ESG performance continued to significantly enhance supply chain resilience. This demonstrates the reliability of the earlier regression results.

5.2.3. Adding Control Variables

To address potential omitted variable bias, two additional control variables were introduced: the number of directors (Board) and the proportion of independent directors (Indep). The number of directors was measured as the natural logarithm of the board size, and the proportion of independent directors was calculated as the number of independent directors divided by the total number of directors. The regression analysis was repeated with these additional controls, and the results are presented in Table 4. Column (3) shows that the estimated coefficient of corporate ESG performance remained significantly positive after adding these control variables, indicating that ESG performance continued to promote supply chain resilience. This was consistent with the earlier regression results, further confirming the robustness and reliability of the baseline findings.

5.2.4. Hausman Tests

To validate the appropriateness of the model specification, this study conducted a Hausman test to compare the fixed effects model with the random effects model. The test results showed that the Hausman statistic was 89.63 (p < 0.001), which significantly rejected the null hypothesis at the 1% level, indicating that the fixed effects model was more suitable for this study.

5.3. Endogeneity Tests

The theoretical model posits that strong corporate ESG performance enhances supply chain resilience. However, a bidirectional causal relationship may exist: firms with greater supply chain resilience likely possess more resource reserves and stronger risk management capabilities, enabling them to allocate more resources to ESG system development. Additionally, enterprises with higher supply chain resilience may face elevated ESG expectations from stakeholders, which in turn pressure them to improve ESG performance. While fixed effects models control for time-invariant unobservable factors, potential endogeneity due to reverse causality remains.

This study employed a two-stage least squares (2SLS) approach to address potential endogeneity between ESG performance and supply chain resilience, using one-period lagged ESG performance (L1.ESG) and two-period lagged ESG performance (L2.ESG) as instrumental variables. The validity of these instruments was rigorously supported by multiple tests: the first-stage regression yielded an F-statistic of 1282.56, far exceeding the Stock–Yogo weak instrument critical value of 19.93 at the 10% significance level, confirming strong relevance between the instruments and the endogenous variable; the Kleibergen–Paap LM test (χ² = 933.56, p < 0.01) rejected the null hypothesis of underidentification, ensuring model identifiability; and the Anderson–Rubin Wald test (F = 5.41, p < 0.01) demonstrated that the causal effect of ESG remains statistically significant even under potential weak instrument risks. These results collectively validate the robustness of the instrumental variable strategy and reinforce the causal interpretation of ESG’s impact on supply chain resilience.

5.4. Mediation Effect Analysis

This study further examined the mechanism through which new quality productive forces mediate the relationship between corporate ESG performance and supply chain resilience. A mediation effect model was employed for this analysis, and the results are presented in

Table 6. Mediation Effect Results.

Variable	Resil	NQPF	Resil
ESG	0.153 *** (0.033)	0.008 *** (0.002)	0.148 *** (0.033)
NQPF	-	-	0.541 *** (0.191)
Cons	0.265 *** (0.043)	0.021 *** (0.003)	0.254 *** (0.044)
Controls	YES	YES	YES
Year	YES	YES	YES
Industry	YES	YES	YES
N	5607	5607	5607
R2	0.116	0.457	0.117
Sobel test	2.225

Note: *** indicate statistical significance at the 1% levels, respectively; standard errors are reported in parentheses.

.

According to the regression results in Table 6, the coefficient of ESG performance on new quality productive forces was significantly positive, indicating that strong ESG performance effectively promotes the enhancement of new quality productive forces. Excellence in environmental, social, and governance practices provides support for technological innovation and productivity improvement, enabling firms to enhance overall productivity through green technology innovation and efficient resource utilization.

Both ESG performance and new quality productive forces have significantly positive effects on supply chain resilience, suggesting that they significantly contribute to improving supply chain resilience. Furthermore, by comparing the data in Columns (1) and (3) of Table 6, it can be observed that the direct effect of ESG performance on supply chain resilience decreases slightly (from 0.153 to 0.148). This change indicates that new quality productive forces play a partial mediating role in the relationship between ESG performance and supply chain resilience. As firms improve their ESG performance, they not only drive green technology innovation and resource efficiency but also provide new momentum for supply chain management and resilience building.

The Sobel test confirmed that new quality productive forces play a significant mediating role between corporate ESG performance and supply chain resilience (indirect effect = 0.0045, Z = 2.225, p = 0.026). After controlling for year and industry fixed effects, ESG performance indirectly enhances supply chain resilience by improving new quality productive forces.

While this study verified the mediating effect of new quality productive forces, other potential mechanisms (e.g., innovation capacity) may also contribute. To address this, R&D investment (measured as the ratio of R&D expenditure to operating revenue) was included as a control variable in robustness checks. The results (

Table 7. Robustness Test of the Mediation Effect.

Variable	Resil	NQPF	Resil
ESG	0.157 *** (0.033)	0.006 *** (0.002)	0.153 *** (0.033)
NQPF	-	-	0.630 *** (0.196)
RD	−0.001 *** (0.000)	0.000 *** (0.000)	−0.001 *** (0.000)
Cons	0.260 *** (0.043)	0.024 *** (0.003)	0.244 *** (0.044)
Controls	YES	YES	YES
Year	YES	YES	YES
Industry	YES	YES	YES
N	5607	5607	5607
R2	0.116	0.477	0.118

Note: *** indicate statistical significance at the 1% levels, respectively; standard errors are reported in parentheses.

) demonstrated that the mediating effect of new quality productive forces remains significant even after accounting for traditional innovation inputs, highlighting its unique role in integrating technological, managerial, and sustainability objectives rather than merely reflecting standalone technological breakthroughs. This finding underscores the distinct advantage of new quality productive forces in synergizing multidimensional capabilities beyond conventional innovation metrics.

5.5. Heterogeneity Analysis

To explore the heterogeneous effects of corporate ESG performance on supply chain resilience, the sample was divided into state-owned enterprises (SOEs) and non-state-owned enterprises (non-SOEs) based on ownership type. Regression analysis was conducted separately for each group using Equation (1), and the results are presented in

Table 8. Heterogeneity Analysis.

Variable	SOEs	Non-SOEs
ESG	0.268 *** (0.067)	0.108 *** (0.039)
Cons	0.170 ** (0.080)	0.314 *** (0.055)
Controls	YES	YES
Year	YES	YES
Industry	YES	YES
N	1766	3841
R2	0.176	0.121
Chow Test	3.28
p-value	0.042

Note: **, and *** indicate statistical significance at the 5%, and 1% levels, respectively; standard errors are reported in parentheses.

.

According to the regression results in Table 8, the coefficients of ESG performance on supply chain resilience were positive for both SOEs and non-SOEs and were statistically significant at the 1% level. This indicated that ESG performance significantly enhances supply chain resilience for both types of enterprises, further validating the conclusions drawn from the earlier regression results. However, the impact of ESG performance on supply chain resilience is more pronounced for SOEs, showing a stronger positive effect. In contrast, while the coefficient for non-SOEs is also positive, it is relatively smaller. The difference was passed through the Chow test at the significance level of 5%, with p value 0.042, and the coefficient difference between groups was significant. This suggests that, under the influence of ownership heterogeneity, the role of ESG performance in promoting supply chain resilience is more significant for SOEs.

The observed differences may arise from the following factors: First, as micro-level vehicles for implementing national strategies, state-owned enterprises (SOEs) have access to greater institutional resources. SOEs enjoy priority in obtaining green credit approvals and issuing special bonds, demonstrate significantly higher success rates in securing government procurement contracts compared to non-SOEs (ensuring stability in supply chain demand), and receive preferential access to government reserve allocations and logistics channels during supply chain disruptions, thereby accelerating recovery capacity building. In contrast, non-SOEs face financing discrimination and technological barriers, leading to fragmented ESG investments. Second, grounded in resource dependence theory, SOEs’ political connections and strategic positions within supply chain networks create synergistic platforms for ESG practices. Their political ties with local governments and industry associations enable prioritized access to ESG-related infrastructure and policy information, allowing rapid adjustments to supply chain configurations. As core nodes in supply chains, SOEs more effectively enhance the transmission efficiency of ESG practices through vertical integration and horizontal alliances. Finally, SOEs’ unique governance structures provide dual safeguards for ESG implementation. The “dual-entry” mechanism between Party committees and boards strengthens top-level strategic alignment in ESG planning, while oversight systems such as SASAC inspections and disciplinary supervision compel SOEs to avoid short-termism. Conversely, driven by shareholder return pressures, non-SOEs often treat ESG as a compliance label rather than a strategic capability-building endeavor.

The empirical finding that “the promoting effect of ESG performance on supply chain resilience is significantly stronger in state-owned enterprises (SOEs) than in non-state-owned enterprises (non-SOEs)” requires careful interpretation within the institutional context. SOEs exhibit greater capacity to address systemic risks due to policy support and structural advantages, whereas non-SOEs enhance resilience through market agility and technological innovation. The resilience-driving mechanisms fundamentally differ between the two ownership types: the former relies on institutional resources, while the latter leverages market-oriented capabilities. Consequently, the stronger ESG impact observed in SOEs may reflect their “policy compliance dividend” rather than intrinsic resilience superiority. Future research should further disentangle “policy-enabled resilience pathways” from “market-driven resilience pathways” to advance both theoretical and practical understanding of ESG–resilience dynamics.

6. Conclusions and Recommendations

6.1. Conclusions

This study, based on data from Chinese A-share listed companies from 2015 to 2023, explores the impact of corporate ESG performance on supply chain resilience and examines the mediating role of new quality productive forces in this relationship. By constructing various econometric models, this study empirically tests how ESG performance enhances supply chain resilience by promoting new quality productive forces. The findings are as follows:

ESG Performance and Supply Chain Resilience: Strong corporate ESG performance significantly improves supply chain resilience, and this conclusion remains valid after robustness tests. Specifically, good ESG performance enhances a firm’s preventive, adaptive, and restorative capabilities, strengthening its ability to withstand external shocks.

Mediating Role of New Quality Productive Forces: New quality productive forces play a significant mediating role in the relationship between ESG performance and supply chain resilience. This indicates that ESG performance indirectly enhances supply chain resilience by fostering the development of new quality productive forces.

Heterogeneity Analysis by Ownership Type: This study finds that the positive impact of ESG performance on supply chain resilience is more pronounced in state-owned enterprises (SOEs) compared to non-SOEs. This suggests that SOEs, benefiting from greater policy support and resource availability, experience more significant improvements in supply chain resilience due to enhanced ESG performance.

This study, based on data from continuously listed Chinese A-share companies, validates the core mechanism through which corporate ESG performance enhances supply chain resilience via new quality productivity. However, it is important to note that the sample selection may be subject to survivorship bias. Delisted companies (e.g., those exiting the market due to poor ESG performance or exposed supply chain vulnerabilities) were excluded from the analysis, which may lead to an overestimation of ESG’s positive impact on resilience. Future research could enhance the robustness of these findings by incorporating data from delisted companies or employing survival analysis models to further verify the conclusions.

6.2. Recommendations

To further promote the synergistic development of corporate ESG performance and supply chain resilience, and to enhance the competitiveness of Chinese enterprises in a complex international environment, this study proposes the following policy recommendations:

1.

Building a “Double Carbon”-Oriented ESG Policy Toolkit

(1)

Carbon Footprint-Driven ESG Rating System: The government should revise the current ESG evaluation standards, incorporate the carbon emission intensity throughout the supply chain lifecycle into core indicators, and set up a tiered reward and punishment mechanism. For example, enterprises that achieve annual carbon reduction targets will be given a 20% increase in green bond issuance quotas (referring to the EU’s Sustainable Finance Classification Scheme), while enterprises that fail to meet the standards will face supply chain financing restrictions.

(2)

“Zero-Carbon Supply Chain” Pilot Project for State-Owned Enterprises: Relying on leading state-owned enterprises, establish zero-carbon supply chain demonstration zones in key industries, requiring them to achieve 100% carbon audit coverage for first-tier suppliers, and develop a blockchain-based carbon data sharing platform to provide replicable technical templates for private enterprises.

2.

Digital Transformation Empowering the Deepening of ESG Practices

(1)

Construction of Supply Chain Digital Twin System: The government supports enterprises in building supply chain digital twin models through special funds to simulate key ESG indicators such as carbon emissions and resource consumption in real time.

(2)

AI-Driven ESG Decision Optimization: Encourage enterprises to use generative artificial intelligence to analyze supply chain ESG data and automatically generate supplier risk assessment reports and emission reduction path plans. Enterprises that adopt such technologies will be given a research and development expense additional deduction ratio increased to 150% as a tax incentive.

3.

Differentiated Policy Design: Solving the Bottlenecks in Private Enterprises’ ESG Implementation

(1)

“Double Carbon” Special Financing Channel: The People’s Bank of China will set up a green re-lending tool for private enterprises, providing preferential loans with an interest rate 50 basis points lower than the loan market quotation rate for small- and medium-sized enterprises that pass ESG certification, while allowing their carbon quotas to be used as collateral.

(2)

State-Owned Enterprise–Private Enterprise Green Technology Alliance: Mandate that state-owned enterprises open up ESG technology sharing platforms in national economic and technological development zones, and private enterprises can use relevant patents for free in the form of “innovation vouchers” to promote technological spillover.

4.

Institutional Guarantees: Strengthening Regulatory and Market Coordination

(1)

ESG Data Governance Legislation: Clarify the collection standards and accountability mechanisms for environmental and social responsibility data of supply chain enterprises in China’s Corporate Sustainable Disclosure Standards—Basic Standards, and implement a “one-vote veto system” for enterprises with data fraud.

(2)

Carbon Tariff Response Fund: The Ministry of Commerce will take the lead in establishing a “Cross-Border Supply Chain Carbon Tariff Compensation Fund”, providing a 50% subsidy for the compliance costs incurred by export enterprises due to the EU’s Carbon Border Adjustment Mechanism, and funding their participation in the formulation of international ESG standards to enhance their voice.