Research on Collaborative Performance of Green Supply Chain Enabled by New Quality Productivity

Abstract

As China’s economy transitions to high-quality development, the traditional growth model proves inadequate for current enterprise challenges. Optimizing resource allocation and cultivating new quality productive forces (NQP) constitute critical enablers of green supply chain collaboration and sustainable business practices. Green supply chain (GSC) collaboration synergizes economic-environmental performance while advancing sustainability. Using longitudinal data (2018–2022) from China’s A-share listed firms, this study examines NQP’s catalytic effects on GSC integration. We operationalized NQP through tripartite metrics (labor inputs, labor objects, and digitized labor processes) and developed a multi-dimensional assessment framework for GSC management integrating economic-environmental criteria. The degree of coupling synergy, calculated through the coupling degree and comprehensive development index, serves as a quantitative measure of GSC collaboration, allowing for a precise assessment of NQP advancement effects. Results demonstrate a significant positive association between NQP development and GSC collaborative performance. Heterogeneity analysis revealed differential impacts, with stronger effects in non-SOEs, technology-intensive sectors, and environmentally sensitive industries. Supply chain digitalization exhibited a moderating effect, strengthening NQP’s influence on GSC outcomes. This research addresses methodological gaps in GSC collaboration measurement and provides novel insights into how NQP drives its synergistic integration. Furthermore, it delivers practical guidance for enterprises to accelerate NQP adoption, optimize GSC partnerships, and operationalize sustainability initiatives.

1. Introduction

New quality productive forces (NQP) has been considered “advanced productivity in line with the new development concept”. Marx’s productive forces framework posits that its tripartite components—laborers, tools, and objects—have undergone qualitative transformations to incorporate new quality productive forces. This modernization manifests through specialized R&D professionals in human capital; eco-intelligent technological systems in production tools; and digitized assets (e.g., data resources) augmenting traditional labor objects. Accordingly, traditional understandings of productive forces can be enriched, and the direction for further economic advancement in China is outlined.

Environmental concerns arising from China’s rapid economic expansion have increased significantly in recent years. The 20th National Congress highlighted the imperatives “to promote economic and social development green, low carbon”. Enterprises, as the main body of the Chinese economy, must respond proactively to these ecological environmental issues. Therefore, the aspiration of corporate value should extend beyond mere shareholder value maximization. A broader perspective necessitates considering multi-stakeholders and integrating the realization of green values into overarching development objectives. Enterprises’ business activities are closely related to their supply chains partners, necessitating inter-firm cooperation for mutual progress. Green and low-carbon principles are integrated into the entire spectrum of corporate productions in addition to business projects through green supply chain management (GSCM). This approach unites nodal enterprises along the supply chain, both upstream and downstream, pooling resources to evaluate the complete supply chain’s operational effect on the environment and the effectiveness of resource distribution. Accordingly, enterprises can achieve economic and environmental benefits. In the entire product lifespan, from the acquisition of raw materials to their disposal management, mitigating negative environmental consequences is essential. A viable strategy is the collaborative governance of green supply chains (GSC) that integrates principles derived from green manufacturing theory with advanced technological applications from the field of supply chain management. This finally allows businesses along the chain to realize both green and economic value. An upgraded iteration of traditional productivity, new quality productivity (NQP) is represented by innovation-driven progress, green and sustainable practices, open integration, and a people-oriented approach [1]. It exerts a positive effect on the cooperation of enterprise GSCs.

Recent studies have predominantly employed questionnaire surveys to assess green supply chain performance, while limited research has quantitatively examined their economic-environmental synergistic effects. Furthermore, although existing literature has extensively explored new quality productivity through lenses of supply chain management and resilience, its implications within collaborative supply chain frameworks remain under explored. Therefore, drawing upon the theoretical underpinnings of NQP, this study evaluates the impact mechanism of enterprises’ NQP on their GSC collaborative performance. The data for this study is derived from A-share listed enterprises (2018 to 2022) and forms the basis of our analysis. Empirical research methodologies, the two-way fixed effects model, rigorously estimate the causal impact of enterprise NQP on GSC collaborative performance. This study addresses critical methodological gaps in evaluating collaborative mechanisms within GSC, while clarifying the catalytic role of NQP in achieving systemic synergies. The investigation provides new empirical insights into the interdependencies between new quality productive forces and supply chain integration processes. The objective is to assist enterprises in advancing the development of NQP and enhancing the collaborative effectiveness of their GSCs.

2. Research Hypothesis

2.1. The Effect of the Development of NQP on the Collaborative Performance of GSC

The evolution of new quality productive forces represents an optimization and advancement of conventional production methodologies in our nation, cultivating high-quality development across various sectors. For enterprises, as the fundamental units of our economy, the necessity to enhance their own new quality productive forces has become a central concern. NQP is defined as innovation-driven, advanced productivity represented by high technology, high efficiency, and high quality, aligning with the principles of the new development concept. Meanwhile, NQP reflects the developmental principles of innovation, coordination, green sustainability, openness, and shared prosperity [1]. The concept of supply chain collaboration was first introduced by Spekman in 1998. Supply chain management has progressed from localized and holistic optimization strategies to the coordinated optimization of both the whole system and its individual components. In addition, an increasing number of enterprises are integrating ecological environmental considerations into their supply chain operations. This integration aims to achieve robust economic performance while reducing negative environmental consequences.

The synergistic integration of GSC driven by NQP operates through four interconnected mechanisms: Technological Empowerment, Collaborative Network Restructuring, Policy-Market Dual Drivers, and Organizational–Cultural Transformation.

At the technological level, IoT sensors, blockchain-enabled carbon traceability systems, and AI-driven optimization algorithms enhance supply chain transparency and resource efficiency. Concurrently, clean energy integration and circular economy technologies establish foundational decarbonization capabilities. The restructuring of collaborative networks manifests through industrial internet platforms that dissolve information asymmetries, coupled with incentive-aligned mechanisms such as carbon revenue-sharing contracts and data-enhanced green financing instruments. Policy-market interactions create dual pressures: regulatory mandates necessitate cross-chain data interoperability, while market signals—including consumer preference shifts and ESG investment imperatives—propel upstream emission reductions. Organizational adaptation occurs via blockchain-based smart contracts enabling decentralized governance and immersive digital tools that institutionalize sustainability cultures.

These mechanisms exhibit dynamic interdependencies: blockchain infrastructures underpin trust in carbon accounting coalitions, regulatory frameworks stimulate carbon data marketization, and distributed decision-making cultures accelerate edge-computing adoption for localized environmental governance. Collectively, they establish a self-reinforcing cycle of data-informed optimization, equitable benefit distribution, regulatory-market responsiveness, and cultural internalization, transitioning supply chains from compliance-driven emissions management to value-creating sustainability ecosystems. Emerging frontiers include predictive risk mitigation through digital twins and self-organizing partner networks governed by adaptive smart contracts, signaling the evolution toward carbon-energy-data ternary markets. This systemic transformation demonstrates how new quality productivity (NQP) repositions green supply chains (GSC) as strategic assets, concurrently achieving cost efficiency, operational resilience, and net-zero alignment. Based on the aforementioned analysis, the study proposes a framework for the collaborative development mechanism of new quality productive forces to promote a GSC. As depicted in the figure below (Figure 1).

Based on the discussion above, H1 is considered as follows. Simultaneously, we will employ a double fixed effects model to empirically test the H1 hypothesis and confirm the reliability of the model through relevant robustness tests.

Hypothesis 1 (H1).

Enterprise development of NQP positively affects GSC collaboration.

2.2. The Regulatory Effect of Supply Chain Digitization

NQP in enterprises creates a conducive environment for GSC collaboration. Supply chain digitization represents a crucial external factor that both accelerates the progression of NQP and strengthens GSC collaboration. The prominence and importance of digitization have grown significantly with advancements in information technologies, e.g., artificial intelligence and cloud computing. It has become a critical driver for economic progress. Enterprises currently have opportunities to digitize their supply chains due to the emergence of these innovative technologies.

Research underscores that supply chain digitization (SCD) critically moderates the relationship between NQP and GSC collaboration through three interrelated mechanisms, while simultaneously advancing innovation-driven principles across value chains. First, data-driven synergy operationalizes NQP’s technological advancements—such as AI-enhanced energy optimization and IoT-enabled production monitoring—into real-time environmental coordination tools, reducing information asymmetry and enhancing upstream–downstream information flow efficiency. Second, dynamic alignment automates compliance processes (e.g., blockchain-based ESG audits) and synchronizes NQP outputs (e.g., smart manufacturing protocols) with partner sustainability goals, thereby lowering transactional friction and fostering stable, long-term interorganizational relationships. Third, risk buffering mitigates collaboration uncertainties (e.g., greenwashing) via digitized verification systems (e.g., lifecycle assessment databases), which validate NQP’s contributions to GSC collaboration through auditable environmental metrics, strengthening partnership consistency and trust. Collectively, these mechanisms amplify GSC collaboration’s responsiveness to NQP by structurally embedding transparency and agility into supply chains, optimizing collaborative sustainability outcomes while reducing partner selection risks and coordination costs—a dual effect that reinforces the innovation-stability nexus in digitized value networks. Moderator analysis assesses how a contextual or contingent factor (moderator) systematically modifies the magnitude and/or direction of the causal relationship between an independent variable and outcome, operationalized through hierarchical regression models incorporating interaction terms and supplemented by post hoc simple slope analyses. The digital transformation of supply chains amplifies the operational efficacy of NQP by structurally embedding its innovative mechanisms, thereby optimizing the implementation of NQP-driven innovations while reinforcing its synergistic integration with GSC systems through dynamic capability alignment. Drawing from the above discussion, the following hypothesis is proposed.

Hypothesis 2 (H2).

Enterprise supply chain digitization positively moderates the relationship between NQP and GSC collaboration.

3. Materials and Methods

3.1. Sample Selection and Data Sources

Considering the research objectives and data accessibility, A-share listed enterprises between 2018 and 2022 had been obtained for our research subjects. The preliminary sample data were subjected to the following screening and processing procedures:

(1)

Companies in the financial sector and those designated as ST (Special Treatment) were removed. Our sample excludes financial institutions and Special Treatment (ST/*ST) firms due to their structural divergence from conventional enterprises. Financial firms exhibit capital-intensive operations with elevated leverage ratios and face prudential regulatory frameworks that distort financial metrics through monetary policy transmission and capital requirements. ST/*ST firms, designated for financial distress or violations, demonstrate extreme financial outliers (e.g., |ROA| > 3 SD) and accounting distortions from going-concern risks, exacerbated by delisting-driven stock anomalies. The aforementioned anomalies can impact the validity of the research outcomes;

(2)

Samples with missing data for relevant indicators were eliminated. Finally, a dataset of 6490 sample observations was compiled. The research data were obtained entirely from the CSMAR database.

3.2. Model Construction

To verify research hypothesis H1, a baseline regression model was formulated as follows:

$${SEGSC}_{it}={\alpha }_0+{\alpha }_1{NQP}_{it}+\sum {Control}_{it}+\sum Industry+\sum Year+{\epsilon }_{it}$$

(1)

In model (1), SEGSC represents enterprise supply chain coordination performance (the dependent variable), while NQP indicates the development of enterprise NQP (the independent variable). Control involves a set of control variables, excluding the impact of factors such as company size, cash flow, debt level, market size, etc., on the model. To factor in variations for individual industry and year, fixed effects for Industry and Year are incorporated. ε represents the random error term. For the purposes of this analysis, i denotes individual enterprises, while t is utilized to represent time periods. Industry-year fixed effects in regression models account for unobserved heterogeneity by controlling for both time-invariant industry characteristics (e.g., regulatory frameworks, technological profiles) and annual macroeconomic or policy shocks affecting all industries. Industry fixed effects absorb persistent cross-sector differences, while year fixed effects capture economy-wide temporal variations. This specification isolates within-industry temporal variation, reducing omitted variable bias and producing estimates that reflect relationships between variables of interest (e.g., firm-level strategies) and outcomes, purged of industry-level and temporal confounders. By focusing on within-industry temporal dynamics, this dual control enhances causal identification and strengthens the internal validity of empirical findings.

3.3. Description of Variables

3.3.1. Dependent Variable

The dependent variable is the Synergistic Effect of Green Supply Chain (SEGSC). Drawing upon the work of Wenlong Yao (2020) [2], we categorize enterprise green supply chains (GSC) collaboration into two dimensions: economic performance and green performance. To evaluate the performance of enterprise green governance, both the positive and negative aspects of corporate involvement in green governance are quantified using the Janis–Fadner coefficient, a commonly used indicator for measuring corporate green performance.

Table 1. Enterprise GSC collaborative performance evaluation index system.

Primary Index	Secondary Index	Indicator Specification
Economic performance	Gross income	Gross operating income
	Total assets	Balance Sheet Total assets
	Return on equity	Net profit/average net worth × 100%
	Net profit	Net profit in the income statement
Green performance	Corporate green governance	J–F coefficient calculated by positive and negative scores of corporate green governance

details the specific evaluation indicators.

From German physics, the concept of coupling refers to the interaction and mutual influence exerted between two or more subsystems, each with unique properties, in a broader system. Additionally, coupling comprises the phenomenon wherein these interconnected subsystems mutually enhance each other’s development. The level of coordination among systems is measured through the coupling coordination degree. Three key variables are central to this model: the coupling degree (C value), the comprehensive development index (T value), and the coupling coordination degree (D value). To assess economic performance, the entropy weight method was employed. Green performance was evaluated with enterprise green governance scores as an indicator. Then, the coupling coordination degree (D value) was applied to evaluate the synergistic relationship specifically between these two subsystem components, namely economic and green performance.

$$D_n=\sqrt{C_n\times T_n}$$

In the above formula, $C_n$ represents the coupling degree of n systems. Let the functions that measure the development levels of multiple systems be U₁, U₂…U_n, then the coupling function of these n systems is as follows:

$$C_n=\sqrt[n]{{\displaystyle \frac{U_1\times U_2\times \dots \times U_n}{{\left(\frac{U_1+U_2+\dots +U_n}{n}\right)}^n}}}$$

The comprehensive evaluation score for the system of n is denoted as T_n,

$$T_n={\propto }_1{U}_1+{\propto }_2{U}_2+\dots +{\propto }_n{U}_n,$$

${\propto }_1,{\propto }_2\dots {\propto }_n$ are coefficients, where ${\propto }_n\in \left[0,1\right],{\propto }_1+{\propto }_2+\dots +{\propto }_n=1,D_n\in \left[0,1\right]$.

A higher degree of coupling coordination indicates greater coordination between systems.

3.3.2. Independent Variables

The independent variable in this research is enterprise new quality productivity (NQP). Based on the foundational elements of productivity in Marxist theory—laborers, labor objects, and labor materials—we constructed an evaluation index system. This system is according to the transformation of these three core elements (

Table 2. NQP evaluation index system.

Primary Index	Secondary Index	Indicator Value Description	Weight	Primary Index Weight
worker	Proportion of R&D personnel	Number of R&D personnel/employees	12.3	18.4
	The proportion of high-level talents	Number of senior executives with a bachelor’s degree or above/number of senior executives	0.8
	CEO functional experience richness	CAMAR database CEO functional background classification standard	5.3
Means of labor	Proportion of fixed assets	Fixed assets/total assets	30.6	59.7
	Proportion of intangible assets	Intangible assets/total assets	14.8
	R&D expenditure	Research and development expenses (RMB) in the profit statement	1.6
	Green investment	Environmental protection amount in management expenses/operating income	1
	Information transparency	Shenzhen Stock Exchange information disclosure evaluation of listed companies	11.7
Object of labor	Number of patents issued	Ln (Number of invention patent applications + 1)	6.6	21.9
	The proportion of green patents	Number of enterprises applying for green patents/number of enterprises applying for patents	12.5
	The degree of data assets	Use text analytics to measure enterprise data assets	2.8

).

Labor reform is principally reflected in the increasing proportion of highly skilled and innovative workers, alongside the shift from manual labor to knowledge-based work. Therefore, drawing on both employee and senior management aspects, three indicators are utilized to quantify labor reform: the proportion of R&D personnel, the ratio of high-quality executives, as well as the extent of CEO functional expertise. Following the method of Hangqin Xiang, Duan Yunlong et al. [3,4] for CEO functional experience richness, and utilizing the CAMAR database classification of CEO functional backgrounds, a statistical approach was applied. Larger values denote richer functional experience.

The means of labor represent the totality of material resources utilized to influence and transform the object of labor. These are essential material conditions in the production process. Labor data reform is mainly reflected in advancements in production tools, the research and application of environmentally friendly equipment, and improved enterprise information sharing through new technologies. Therefore, five indicators are employed to measure labor data reform. These include the following: the ratio of fixed or intangible assets, research and development expenditure, green investment, and information transparency. Green investment, following the methodology of T. Nigatu, Zhao Lingdi et al. [5,6], is defined as environmental protection-related expenses identified in the schedule of management expenses, standardized by dividing by business income. This reflects the proportion of enterprise green investment.

The object of labor is the direct focus of production activities. With scientific and technological advancement, changes in the labor object are reflected in the innovation of labor means and the development and utilization of novel resources. Hence, three indicators are adopted to measure changes in the labor object: the number of patents published, the proportion of green patents, and the degree of data assets. Referencing the method of Ying He et al. [7], we developed a data capitalization keyword set. Word frequency analysis of terms similar to this set in enterprise annual reports was conducted. The total word frequency was logarithmically transformed to represent the enterprise data capitalization degree. Higher values indicate a greater degree of data capitalization.

For an objective weighting in the established evaluation index system of enterprise NQP, the entropy method measures the index weight. The observed weight distribution in evaluating new quality productive forces—laborers (18.4%), production tools (59.7%), and labor objects (21.9%)—reflects the technology-driven paradigm of modern productivity systems. This allocation underscores the dominance of advanced production tools, such as AI, IoT, and automation technologies, which account for nearly 60% of productivity contributions by enabling exponential efficiency gains and redefining value creation mechanisms. While laborers retain an 18.4% share, their role transitions from direct manual engagement to high-skilled technical coordination and innovation management, demonstrating a qualitative shift in human capital utilization. Labor objects, constituting 21.9%, gain enhanced significance through datafication and resource optimization, though their value realization remains contingent on technological mediation. The disproportional weighting of production tools highlights the structural transformation from traditional labor-intensive models to a techno-capitalist framework, where digital infrastructure and intelligent systems serve as primary productivity multipliers. This configuration aligns with the evolutionary trajectory of Industry 4.0 economies, wherein technological capital’s marginal productivity surpasses conventional factors, fundamentally reconfiguring the input–output dynamics of value chains. Based on these calculated weights, we then evaluate the level of enterprise NQP [8].

3.3.3. Adjust Variable

Considering supply chain digitization (Treat × Time) as a moderating variable, the effect of NQP development on supply chain collaborative performance is evaluated in this paper, specifically in supply chain digitization.

In 2018, a crucial initiative was launched by eight departments, spearheaded by the Ministry of Commerce, to advance supply chain innovation and application pilot programs. Supply chain digitization was central to these programs. Supply chain digitization is increasingly recognized as a critical moderating variable in enhancing the nexus between (NQP) and GSC collaboration. By embedding innovation-driven principles, supply chain digitization reduces information asymmetry and transaction costs through real-time monitoring (e.g., IoT-enabled resource tracking) and automated compliance mechanisms (e.g., blockchain for ESG alignment), thereby translating NQP’s technological advancements—such as AI-driven energy optimization—into actionable sustainability initiatives. Furthermore, digitized platforms mitigate collaboration risks (e.g., greenwashing) by standardizing auditable environmental metrics (e.g., lifecycle assessments), which strengthen interorganizational trust and partnership longevity. These mechanisms collectively amplify GSC collaboration’s responsiveness to NQP innovations, fostering stable, agile, and transparency-driven value chains while optimizing resource orchestration for sustainability outcomes.

Therefore, to quantify supply chain digitization, we adopt the measurement approach developed by Zhang Shushan et al. (2021) [9], utilizing virtual variables. An enterprise designated as a supply chain innovation and application pilot enterprise is assigned a value of 1; otherwise, the value is 0.

3.3.4. Control Variables

Based on existing works, this research incorporates several control variables, including enterprise size (Size), asset–liability ratio (Lev), cash flow ratio (Cashflow), board size (Board), financial leverage (FL), and the proportion of the largest shareholder (Top1).

Table 3. Meaning of variables.

Property of Variable	Variable Name	Variable Symbol	Variable Declaration
Dependent variable	Collaborative performance of enterprise GSC	SEGSC	The coupling coordination degree of enterprise green performance and economic performance
Independent variables	Enterprise NQP	NQP	The comprehensive index calculated by entropy weight method
Adjust variables	Supply chain digitization	Treat	The value is 1 when the enterprise belongs to the supply chain innovation and application pilot enterprise, and 0 when the enterprise is not
Control variables	Enterprise scale	Size	The natural log of total assets per year
	Asset-liability ratio	Lev	Total liabilities at year-end/total assets at year-end
	Cash flow ratio	Cashflow	Net cash flows from operating activities/total assets
	Board size	Board	Take the natural logarithm of the number of board members
	Financial leverage	FL	(Net Income + Tax Expense + Finance expense)/(Net income + Tax expense)
	The proportion of the largest shareholder	Top1	Number of shares held by the largest shareholder/total number of shares
	Tobin’s Q value	TobinQ	(Market value of tradable shares + Number of non-tradable shares x Net asset value per share + Carrying amount of liabilities)/total assets

offers a detailed explanation of the specific meaning of each of these variables.

4. Empirical Research and Results

4.1. Descriptive Statistics

Table 4. Descriptive statistics of major variables.

Variable Name	Sample Size	Mean Value	Standard Deviation	Minimum Value	Maximum Value
SEGSC	6490	0.365	0.055	0.110	0.922
NQP	6490	0.147	0.033	0.052	0.472
Size	6490	22.235	1.204	19.807	26.452
Lev	6490	0.399	0.178	0.051	0.902
Cashflow	6490	0.056	0.062	−0.167	0.267
Board	6490	2.087	0.191	1.609	2.639
FL	6490	1.182	0.708	−1.513	6.745
Top1	6490	30.892	13.964	8.020	73.984
TobinQ	6490	2.044	1.278	0.802	9.817

reports the descriptive statistics for the main variables. The dependent variable, NQP (Enterprise New Quality Productive forces), exhibits an average value of 0.147, alongside a significant difference between its minimum and maximum values. Additionally, a major difference exists between the mean and maximum values, suggesting significant heterogeneity in the level of NQP across Chinese enterprises. These data also indicate that the overall NQP level among Chinese enterprises is relatively low. The wide range observed between the minimum and maximum values of the independent variable, SEGSC (Synergistic Effect of Green Supply Chain),demonstrates a broad sample coverage for this study, enhancing the robustness of our research conclusions. Moreover, the selected control variables display significant difference between their minimum and maximum values, which is beneficial for effectively controlling for extraneous factors in this research.

Table 5. Collinearity test.

Variable Name	VIF
NQP	1.140
Size	1.610
Lev	1.470
TobinQ	1.160
Cashflow	1.140
FL	1.090
Board	1.080
Top1	1.040
MeanVIF	1.220

presents the results of collinearity tests conducted among the variables. The analysis indicates that VIF values range from 1.04 to 1.61. These findings indicate that multicollinearity is not a concern among the variables selected for this study, thus satisfying the conditions necessary for our analysis.

4.2. Baseline Regression

Table 6. Results of baseline regression.

Variables	SEGSC
	(1)	(2)
NQP	0.645 ***	0.274 ***
	(7.12)	(3.75)
Size		0.028 ***
		(16.82)
Lev		0.005
		(1.25)
Cashflow		0.018 **
		(2.78)
Board		−0.001
		(−0.16)
FL		−0.002 ***
		(−3.43)
Top1		0.000 ***
		(7.26)
TobinQ		−0.000 **
		(−2.55)
Constant term	0.270 ***	−0.299 ***
	(20.37)	(−7.02)
Industry	Controls	Controls
Year	Controls	Controls
Sample size	6490	6490
R2	0.226	0.527
Adjust R2	0.224	0.525

Note: **, and *** are significant at the 5%, and 1% levels, respectively.

details the benchmark regression results, confirming the positive impact of NQP on SEGSC. Column (1) displays the regression result when only the relationship between NQP and enterprise GSC collaborative performance is examined. Column (2), in comparison, demonstrates the regression result after incorporating various control variables. The results consistently demonstrate significantly positive coefficients for enterprise NQP. Control variables, however, appear to exert minimal effect on these results. This suggests that advancements in enterprise NQP significantly and positively increase enhancing the collaborative performance of enterprise GSCs, thereby supporting Hypothesis H1.The statistically insignificant effects of Lev and Board on SEGSC can be attributed to temporal and governance misalignments. For Lev, SEGSC is primarily driven by exogenous institutional pressures (e.g., environmental regulations), endogenous strategic priorities (e.g., CSR commitments), and market incentives (e.g., sustainable consumer demand), rather than short-term financial structures. Debt ratios, which fluctuate with macroeconomic cycles, conflict with the long-term resource stability required for SEGSC investments. For Board, a governance efficiency paradox emerges: larger boards face procedural delays and conflicting stakeholder interests that hinder agile sustainability responses, whereas smaller boards lack multidisciplinary expertise critical for complex green supply chain decisions. Furthermore, SEGSC implementation is typically delegated to specialized managerial units (e.g., sustainability committees), diluting the direct governance impact of board size. These findings align with institutional theory, emphasizing external isomorphism over internal financial or structural factors, and resource dependence theory, highlighting the contingent role of governance configurations rather than size alone in sustainability transitions.

4.3. Robustness Test

The Replacement of the Dependent Variable

Table 7. Robustness test—Replace variables and remove abnormal data.

	(1)	(2)	(3)
	Replace the Dependent Variable SCE	Replace Independent Variables SEGSC	Eliminate Abnormal Cities SEGSC
NQP	1.694 ***		0.274 ***
	(4.20)		(3.75)
TFP_ACF		0.007 ***
		(4.76)
Constant term	5.697 ***	−0.322 ***	−0.299 ***
	(17.15)	(−6.36)	(−7.02)
Control variables	Controls	Controls	Controls
Fixed effect	Controls	Controls	Controls
Sample size	6490	6490	3770
Adjust R2	0.254	0.508	0.525

Note: *** indicates statistical significance at the 1% level (p < 0.01).

confirms that the positive impact of NQP on SEGSC remains significant after substituting variables and eliminating outlier data.

(1)

The replacement of the dependent variable

In the benchmark regression analysis, the collaboration of the enterprise GSC was measured with the coupling synergy degree of enterprise green governance score and economic performance. To further confirm the effect of enterprise NQP on GSC collaboration, we referred to the methodology of Zhang Shushan et al. (2023) [10]. Supply chain efficiency (ln) (365/inventory turnover) was utilized as an alternative dependent variable. Inventory turnover days measure the frequency of exchange between upstream and downstream supply chain partners, reflecting both supply chain responsiveness and inventory liquidation speed. In addition, it captures the efficiency of logistics, information flow, and capital flow in supply chain enterprises. The Npro coefficient remains significantly positive at the 1% level, as presented in Table 7 (1). This suggests that even with a different dependent variable, the development of NQP in enterprises continues to positively affect the improvement of GSC collaborative performance, offering further validation for Hypothesis H1.

(2)

Alternate independent variable

Productivity, in economic research, can be effectively represented by total factor productivity. As a crucial measurement for economic development, total factor productivity reflects the contributions of systemic factors, technological advancements, and other elements to economic growth. Accordingly, and referencing Song Jia et al. [11], we performed a regression analysis utilizing total factor productivity calculated through the ACF method. The results, demonstrated in column (2) of Table 7, indicate that the TFP_ACF coefficient is significantly positive at the 1% level. This demonstrates that even when employing an alternative independent variable, the development of NQP in enterprises maintains a significant positive effect on enterprise GSC performance, further verifying Hypothesis H1.

(3)

The elimination of abnormal cities

The economic conditions of provincial capitals and municipalities directly under the Central government in China present unique characteristics compared to typical provincial capitals. The exclusion of provincial capitals and municipalities directly under central government in robustness checks is motivated by their unique socioeconomic and institutional characteristics that may confound the relationship between NQP and SEGSC. First, these cities typically benefit from disproportionate policy privileges (e.g., targeted subsidies, national pilot programs) and resource concentration (e.g., talent, capital), leading to systematically higher NQP levels that deviate from conventional urban development patterns, thereby introducing outliers and skewing parameter estimates. Second, their economic structures often prioritize service sectors and administrative functions rather than manufacturing or traditional industries, creating heterogeneity that obscures generalizable NQP mechanisms. Third, as preferred locations for policy experimentation (e.g., free trade zones, digital economy hubs), their NQP growth may reflect exogenous policy interventions rather than endogenous market dynamics, raising endogeneity concerns. By excluding these atypical cases, the analysis ensures robustness against extreme values, mitigates bias from political hierarchy-driven resource allocation, and enhances external validity for ordinary prefecture-level cities. To reduce potential biases in our research findings, we followed the approach of Song Jia et al. [11] (2024) and excluded enterprise samples from the four municipalities directly under the central government—Beijing, Shanghai, Tianjin, and Chongqing—and 27 provincial capital cities, before conducting regression analysis. The Npro coefficient remains significantly positive at the 1% level, as presented in Table 7 (3), implying that the enhancement of GSC collaborative performance among enterprises across various regions of China promotes the development of NQP, thus confirming H1.

(4)

Endogeneity test

To test potential endogeneity issues, such as bidirectional causality and omitted variable bias, we employed the instrumental variable method.

Table 8. Robustness test—Endogeneity test.

Variables	(1)	(2)
	The First Stage	The Second Stage
TFP_OP	0.0097 ***
	(7.1372)
NQP		1.1483 ***
		(4.2823)
Kleibergen-Paap rk LM statistic	1.864 (0.000)
Kleibergen-Paap rk Wald F statistic	50.940
Control variables	YES	YES
Fixed effect	YES	YES
Sample size	6490	6490
R2		0.9768

Note: *** indicates statistical significance at the 1% level (p < 0.01).

confirms the validity of the instrumental variables and reiterates the positive impact of NQP on SEGSC. Specifically, total factor productivity (TFP_OP), calculated utilizing the OP method, was adopted as the instrumental variable for this endogeneity test. This study employs TFP as an instrumental variable based on two primary rationales grounded in econometric theory. First, relevance condition: Within the framework of production function analysis, TFP is intrinsically linked to the capital and labor dimensions of new-quality productivity, thereby establishing a statistically robust association with the endogenous variable of interest. Second, exogeneity condition: TFP-driven advancements in new-quality productivity are predominantly mediated by exogenous technological progress rather than unobserved mechanisms (e.g., managerial discretion or policy interventions). This exclusion restriction ensures that TFP influences green supply chain performance solely through its effect on new-quality productivity, circumventing confounding pathways that might violate causal identification. In the 2SLS test, the Kleibergen-Paap rk LM statistic significantly rejects the unrecognizable hypothesis, as demonstrated in column (1) of Table 8. In addition, the Kleibergen–Paap rk Wald F statistic surpasses the threshold for the weak instrument variable test, confirming the validity of our chosen instrumental variable. After incorporating TFP_OP as an instrumental variable, enterprise NQP continues to exhibit a positive effect on GSC collaboration, according to column (2) of Table 8.

4.4. Further Analysis

4.4.1. Adjustment Effect Test

Following our prior examination of NQP’s impact and operational processes on collaborative performance in the Global Supply Chain (GSC), we proceed to assess the moderating influence of enterprise supply chain digitalization. In this section, we further investigate the extent to which digitalization in an enterprise’s supply chain affects the relationship between NQP and GSC collaboration.

Enterprises’ pursuit of NQP is closely related to innovation and development. In effect, the present era, represented by a new wave of scientific and technological revolution alongside industrial transformation, highlights digital technology iteration as a crucial driver for achieving technological innovation. This digital advancement ensures improvements in enterprises’ NQP. Enterprise supply chain digitalization, accordingly, prioritizes the implementation of digital tools, optimized technologies, and robust cooperation alongside information exchange among stakeholders. Such digitalization enhances operational and managerial effectiveness, cultivating the progression of NQP and, therefore, positively affecting GSC cooperation. The digitization enhances information flow efficiency and diminishes transaction costs, thereby fundamentally transforming enterprise operations and production processes. By optimizing data-driven communication channels both internally and externally, this technological shift elevates the precision and speed of information exchange while mitigating transmission delays. Concurrently, it reduces operational ambiguities by establishing more reliable decision-making frameworks through improved data accessibility and analytical capabilities. These systemic improvements collectively streamline business workflows, enhance cross-functional coordination, and strengthen organizational responsiveness to market dynamics [12].

Table 9. Test results of adjustment effect.

	(1)	(2)
	SEGSC	SEGSC
NQP	0.274 ***	0.224 **
	(3.75)	(2.63)
SEGSC × Treat		0.655 ***
		(3.68)
Treat		0.087 ***
		(3.35)
Constant term	−0.299 ***	−0.272 ***
	(−7.02)	(−5.08)
Control variables	YES	YES
Year fixed effect	YES	YES
Industry fixed effect	YES	YES
Sample size	6490	6490
R2	0.527	0.541

Note: **, and *** are significant at the 5%, and 1% levels, respectively.

confirms our hypothesis. To quantify supply chain digitalization, and in accordance with Zhang Sushan et al. (2021) [9], a virtual variable was employed. This variable assigns a value of 1 to enterprises recognized as pilot enterprises in supply chain innovation and application, and 0 otherwise. The findings presented in column (2) of Table 9 indicate a significantly positive coefficient (p < 0.01) for the interaction term (SEGSC × Treat) between enterprise GSC collaboration and supply chain digitalization. This result offers empirical support for Hypothesis H2, confirming that enterprise supply chain digitalization strengthens the positive relationship between NQP and GSC collaboration.

4.4.2. Heterogeneity Analysis

This study presents in

Table 10. Heterogeneity analysis.

Variables and Statistical Parameters	Equity		Industry Attribute		Environmental Sensitivity
	State-Owned Enterprise	Non-State-Owned Enterprise	High-Tech Industry	Non-High-Tech Industry	Heavy Pollution Industry	Non-Heavy Polluting Industries
	(1)	(2)	(3)	(4)	(5)	(6)
NQP	0.459 ***	0.129 ***	0.243 **	0.355 **	0.133	0.305 ***
	(3.19)	(3.24)	(3.05)	(2.23)	(2.55)	(3.18)
Constant term	−0.579 ***	−0.173 ***	−0.214 ***	−0.382 ***	−0.306 *	−0.286 ***
	(−6.38)	(−12.17)	(−15.64)	(−5.35)	(−3.28)	(−8.04)
Control variables	Controls	Controls	Controls	Controls	Controls	Controls
Fixed effect	Controls	Controls	Controls	Controls	Controls	Controls
Sample size	1413	5077	3559	2931	1818	4672
Adjust R2	0.666	0.409	0.465	0.582	0.520	0.532

Note: *, **, and *** are significant at the 10%, 5%, and 1% levels, respectively.

the differences in the effect of NQP on SEGSC across enterprises with different property rights and industry attributes.

(1)

Differences in enterprise property rights

Differences in enterprise property rights are expected to affect their respective business objectives and business models. China’s state-owned enterprises (SOEs) and non-SOEs differ fundamentally in governance, resources, and behavior. SOEs, government-owned and controlled, prioritize policy goals (e.g., social stability, strategic sectors) with access to subsidized financing and regulatory privileges. Non-SOEs, driven by market competition, focus on profitability and innovation—producing over 70% of patented technologies—but face financing constraints and regulatory risks. SOEs exhibit risk-averse decision-making under political–economic dual mandates, while non-SOEs adopt flexible strategies despite market uncertainties. State-owned enterprises (SOEs) exhibit institutional isomorphic pressures stemming from governmental regulatory compliance mandates, manifesting in GSC collaboration practices that primarily serve political legitimacy objectives through normative policy alignment. Conversely, non-state-owned enterprises (NSOEs) operate within competitive market logics necessitated by multi-stakeholder accountability, wherein GSC collaboration implementation reflects a strategic calibration between operational efficiency optimization and reputational capital accumulation, consistent with resource dependence theory postulates. Thus, varying property rights structures result in distinct business strategies and objectives. Simultaneously, government subsidies can produce asymmetric effects on green innovation, potentially influencing research outcomes. For this heterogeneity analysis, we categorized enterprises into SOE (SOE) (SOE = 1) and non-SOE (NSOE) (SOE = 0). Analysis of columns (1) and (2) within Table 10 reveals that SEGSC coefficients are significantly positive at the 1% level for both SOE and NSOE categories. These significantly positive coefficients at the 1% level across both property rights classifications indicate a correlation between increased NQP and enhanced GSC collaboration. However, a notable observation is the greater absolute value of the SEGSC coefficient specifically in the non-state-owned enterprise sample. This observation implies that the positive influence of NQP on GSC collaborative performance is more significant in NSOEs.

First and foremost, SOE, aligning closely with national directives, bear greater national policy responsibilities. This alignment facilitates a smoother adoption of relevant scientific and technological advancements. In effect, the implementation of NQP concepts tends to occur earlier in SOE. In addition, governmental policies and measures aimed at enhancing NQP to promote GSC coordination are often more standardized for SOE compared to their non-state-owned counterparts, where national guidance is less direct. Facing greater limitations in resources and challenges in investment and financing, NSOE must overcome greater challenges to cultivate their NQP development. Meanwhile, NSOE exhibit a stronger urgency to drive innovation, aiming to leverage advancements and improved green practices to enhance their public image. Secondly, state-owned enterprise supply chains generally exhibit a higher level of cooperation, whereas supply chain coordination in NSOE necessitates continuous engagement between the enterprises themselves, suppliers, and consumers. Improvements in supply chain coordination for SOE are frequently propelled by the state, while NSOE depend more on advancements in NQP to achieve similar supply chain synergy. Therefore, the effect of NQP on GSC collaborative performance is observed to be weaker in SOE when compared to NSOE.

(2)

Industry attribute difference

Industry nature may moderate the effect of NQP on GSC collaborative performance, as enterprises in different industries operate with varying business foci and models. To test how industry characteristics affect our findings, we evaluated two dimensions: high-tech industry classification and heavy polluting industry designation.

① Technical differences

High-tech industries, particularly those operating in emerging sectors such as renewable energy and semiconductor manufacturing, demonstrate superior adaptability within the technology-organization-environment (TOE) framework. This enables these industries to rapidly assimilate emerging technological innovations, including artificial intelligence (AI) and Internet of Things (IoT) systems. Conversely, non-high-tech industries face substantial barriers to green transformation, including prohibitive technology conversion costs and critical shortages of technical expertise. These non-high-tech industries consequently adopt a more gradualist approach to GSC management, relying on incremental process optimizations rather than disruptive technological implementations. Their environmental strategies typically emphasize evolutionary improvements in existing operational parameters rather than the systematically coordinated approach enabled by advanced digital infrastructure in technology-intensive sectors.

Drawing upon the methodology of current research, coupled with the 2012 industry classification standards issued by the China Securities Regulatory Commission and the “High-tech Fields Supported by the State” directive, a binary classification system was established. Enterprises identified as belonging to high-tech industries were assigned a value of 1 under this system, while all others received a value of 0. Analysis of the data presented in columns (3) and (4) of Table 10 reveals that the Npro coefficient exhibits a significant positive correlation at the 5% level for both high-tech and non-high-tech enterprise groups. This suggests that industry technological differences do not negate the positive effect of NQP on GSC collaborative performance. However, it is worth noting that the absolute value of the Npro coefficient is larger for high-tech enterprises. This observation implies a more significant effect of NQP on GSC collaboration in high-tech industries.

This is due to the fact that high-tech industries are defined by a greater intensity of knowledge and technology compared to non-high-tech sectors, affording them advantages in digital technology application. Simultaneously, the high-tech industry is characterized by higher risks and volatility due to various factors, including technological innovation, short product life cycles, rapid delivery lead times, frequent changes in demand for high-quality yet affordable products, as well as complexity and uncertainty [13]. The development of NQP, strengthened by robust enterprise digital systems, can maximize its synergistic effect on supply chain performance. Therefore, NQP levels in high-tech enterprises demonstrate a strong synergistic influence on GSCs; however, non-high-tech enterprises often lag behind in technology, talent acquisition, and innovation capacity, rendering technological breakthroughs more challenging. Therefore, the development of NQP exerts a comparatively weaker influence on GSC collaborative performance in non-high-tech industries.

② Environmental sensitivity difference

The environmental sensitivity of an enterprise’s industry is a factor that affects their environmental performance and governance practices. Heavily polluting industries, such as chemical production and steel manufacturing, face stringent environmental regulatory pressures, including mandatory participation in carbon emissions trading schemes. Their engagement in green supply chain (GSC) collaboration is primarily compliance-driven, reflecting an existential imperative to adopt transition technologies to meet regulatory thresholds and avoid operational penalties. This reactive adaptation contrasts sharply with non-polluting industries, which approach GSC collaboration through a proactive strategic orientation. These industries prioritize voluntary sustainability initiatives aligned with long-term competitive positioning, leveraging green practices as a means of enhancing brand equity and preempting future regulatory shifts. In addition, the pace and magnitude of NQP’s development vary across industries with differing environmental sensitivities. Similarly, the priorities for GSC collaborative development differ across these industries. Accordingly, the extent to which NQP enhances GSC collaboration performance differs based on industry environmental sensitivity. To categorize enterprises based on their pollution levels, we adopted the methodology of Li Jinglin (2021) [14]. This categorization differentiated between heavy polluting and non-heavy polluting industries. Our classification was informed by the 2012 Guidance on Industry Classification of Listed Companies, revised by the China Securities Regulatory Commission, in conjunction with the Classified Management List of Environmental Protection Verification Industries of Listed Companies, issued by the Ministry of Environmental Protection. Then, we investigated whether the impact of NQP on GSC collaborative performance exhibited variations depending on the environmental sensitivity of the enterprise. The outcomes of these tests are presented in columns (5) and (6) of Table 10. In the sample of heavy polluting industries, the Npro coefficient demonstrated non-significance. Conversely, a significant and positive coefficient at the 1% level was observed for enterprises categorized in non-heavy polluting industries. These results indicate that the positive effect of NQP on GSC collaborative performance is less influencing for enterprises in heavy polluting industries when contrasted with those in non-heavy polluting sectors.

This observation can be attributed to the greater public, governmental, and stakeholder scrutiny directed toward the GSCs of enterprises in heavily polluting industries. Public attention and related factors exert a stronger influence on these enterprises’ focus on GSC, leading to greater improvements driven by external pressures and higher compliance demands. Therefore, NQP development has a comparatively reduced effect on GSC collaborative performance in heavily polluting industries; whereas, in non-heavy polluting industries, NQP exerts a more pronounced influence on improving GSC collaborative performance. Improvements in NQP drive these enterprises to prioritize innovation and collaboration, thereby cultivating digital technology advancements and green development and resulting in a more significant effect on GSC collaborative performance. Therefore, the positive effect of NQP on GSC collaboration is more significant for enterprises in non-heavy polluting industries compared to their counterparts in polluting industries.

5. Discussion

5.1. The Connotation of New Quality Productive Forces and Its Impact

Extensive academic discussion has been dedicated to explaining the connotation of NQP, offering a robust theoretical foundation for the following analyses into this concept. Certain academics have adopted singular perspectives in their interpretations. For instance, Zhong Maochu (2024) hypothesizes that NQP represents an advanced form of productivity where “disruptive innovation technology” supersedes capital-driven growth [15]; whereas, Zhai Qing and Cao Shouxin (2024), grounding their analysis in Marxist productivity theory, define NQP as the enhancement of product supply efficiency and the industrial structure transformation and upgrading [16]. This is achieved by exploring novel production factors, optimizing the quality of traditional production factors, and improving overall alignment efficiency [16]. Alternatively, several researchers have explored the concept by analyzing its fundamental principles alongside its similarities and differences from traditional productivity. Pu Qingping and Xiang Wang (2024) propose that NQP comprises advancements in the productivity of highly skilled laborers, novel intermediate labor materials, and innovative, high-quality objects of labor [17]. Similarly, Shi Jianxun and Xu Ling (2024) [18] argue that NQP is underpinned by emergent technologies, e.g., big data, cloud computing, as well as green and low-carbon solutions. Hongman Liu et al. (2025) posit that new quality productive forces fundamentally distinguish themselves from traditional productive forces by transcending mere quantitative productivity expansion [19]. Their paradigm shift manifests in qualitative structural shifts that necessitate a multidimensional alignment of ecological preservation with socioeconomic systems, thereby redefining sustainable development paradigms [19]. In addition, they emphasize its close integration with new productivity means including digital intelligent labor, digital infrastructure, and new energy sources [18]. Thus, the new productivity is grounded in the current state of China’s economic development, extending the fundamental theory of traditional productivity, and the enhancement of traditional productivity workers, labor materials, and labor tools.

From the perspective of the impact of new productivity, Li Lin et al. (2024) conducted an empirical analysis exploring the potential nonlinear effects of advancing new quality productivity on enhancing high-quality agricultural development [20]. Their work contributes significantly to the empirical understanding of how new quality productivity fosters the development of high-quality agriculture [20]; Changhua Shao et al. (2024) examined how new quality productivity impacts China’s industrial restructuring while incorporating environmental regulation to clarify the interaction mechanisms between these factors [21]; Jiajun Zhang et al. (2024) focused on 30 Chinese provinces and cities by employing the entropy method alongside the Slack-Based Measure directional distance function model. This approach, which accounts for non-expected outputs, allowed them to measure new quality productivity and manufacturing carbon emission efficiency between 2012 and 2021 [22]. They further applied an enhanced coupling coordination degree model and relative development model to evaluate the harmonious development between these factors [22]; through analysis of panel data covering 30 Chinese provinces and cities from 2012 to 2022, Yi Liu et al. (2024) determined that synergistic agglomeration significantly promotes high-quality development in the manufacturing industry [23]. Their findings indicated that ‘new quality productive forces’ function with a critical intermediary effect, while the innovation ecosystem exerts both direct and indirect moderating effects on the relationship between these elements [23].

5.2. Research on the Green Supply Chain

Research concerning green supply chains have primarily concentrated on areas consisting of measurement, influencing factors, and other related aspects. From the perspective of measuring GSC, in their publication, Apeji, Uje D. et al. (2020) introduce a methodology for measuring visibility in sustainable supply chains [24]. This methodology is grounded in Shannon’s information entropy [24]. Through a survey of 320 manufacturing organizations, T. C. Edwin Cheng (2021) discovers that circular economy practices enhance sustainable supply chain flexibility, while big data analytics was determined to complement such flexibilities [25]. The research by Jianmin Sun et al. (2022) demonstrates a positive correlation between a sustainable supply chain strategy and achieving a sustainable competitive advantage [26]; Zhang (2018) confirms the multi-dimensional nature of sustainable supply chain management. This finding implies that future research should involve both environmental and social dimensions [27]; Laguir et al. (2025) contends that supply chain dynamism positively affects both supply chain ambidexterity and sustainable performance indicators [28]. Their research also suggests that supply chain exploitation has a greater contribution to environmental performance compared to social and economic indicators of sustainable performance [28]. From the perspective of driving factors of GSC, Zulkaif Ahmed Saqib et al. (2025) propose a comprehensive multidimensional framework that demonstrates the mechanisms through which electronic word-of-mouth (eWOM) mitigates information asymmetry, strengthens stakeholder credibility, facilitates data-driven decision-making, and ultimately promotes environmentally conscious consumer behavior [29]. Zulkaif Ahmed Saqib, Gang Xu, and Qin Luo’s (2024) empirical analysis demonstrates that organizations exhibiting strategic integration of operational transparency with sustainability commitments demonstrate enhanced capability in addressing concurrent operational challenges and sustainability-related constraints [30].

Yang et al. (2013) describing green supply chain integration and collaboration as a means to promote efficiency and synergy among business partners and an approach to strengthen corporations [31]. The contemporary paradigm of production systems has transcended the operational purview of individual enterprises, driven by the intensification of economic globalization. This systemic shift necessitates cross-tier coordination between upstream and downstream entities within supply chain networks to address the environmental externalities inherent in modern manufacturing processes. The institutionalization of sustainability principles across supply chain operations—encompassing procurement, production, and distribution—engenders an evolved collaborative framework termed GSC collaboration. This governance model enables the synergistic optimization of economic performance and ecological stewardship through aligned resource allocation and standardized environmental protocols [32].

As an evolved form of traditional productivity, NQP exhibits several unique characteristics such as digitalization, intelligence, green practices, and coordination. Therefore, the advancement of NQP aligns with the principles of GSC coordination, exerting a significantly positive effect on the enhancement of GSC coordination performance. Existing research concerning the development of NQP-enabled supply chains primarily centers on supply chain’s security, resilience, as well as modernization. Zulkaif Ahmed Saqib and Qingyu Zhang (2021) point out that the implementation of sustainability-oriented practices—particularly in manufacturing processes, procurement strategies, and distribution channels—exerts a significant positive effect on a firm’s environmental and operational performance metrics [33]. Notably, this causal relationship is subject to the moderating influence of supply chain visibility, with higher levels of end-to-end transparency strengthening the magnitude of the association between sustainable initiatives and performance outcomes [33]. Zhang Xiaheng (2025) contends that NQP can strengthen supply chain resilience through mechanisms such as driving innovation, elevating labor quality, optimizing resource allocation, and strengthening risk management capabilities [34].

Complementing this, Zhang Wen et al. (2024) suggest that NQP acts as an intermediary factor in the modernization of innovation-enabled supply chains [35]. Zulkaif Ahmed Saqib and Luo Qin (2024) found that strategic adoption of digital innovation technologies enhances the sustainability of supply chain operations, thereby contributing to measurable gains in long-term organizational competitiveness [36]. This study empirically demonstrates that digitally driven process optimization—spanning resource allocation, emissions reduction, and circular economy implementation—serves as a catalyst for aligning ecological stewardship with economic value creation [36]. Shi Man (2024) proposes that NQP constitutes a critical intermediate pathway through which artificial intelligence affects supply chain security [37]; according to Feng Yang et al. (2024), this study utilized data from publicly listed automotive manufacturing firms between 2009 and 2022 to examine the impact of target firms’ environmental, social, and governance (ESG) performance on total factor productivity (TFP) at upstream and downstream firms from a supply chain perspective [38]. Certain academics have broadened their scope to study the effect of NQP from diverse supply chain perspectives, including logistics. For instance, Tang Demin et al. (2024), based on NQP, proposed a revised liquor sales logistics management model [39]. This model, according to the convergence of GSC principles and liquor sales logistics management reconstruction, is expected to produce positive effects such as cost reduction, energy conservation and emission reduction, and expedited distribution times [39].

5.3. Literature Review

Current studies into supply chain development driven by NQP primarily appraise the effect of NQP on overall supply chain advancement, or specific facets thereof. A review of research content indicates a relative scarcity of literature addressing the sustainable development dimension of supply chains. From a research perspective, studies have primarily focused on aspects such as supply chain security, resilience, modernization, and the consideration of multiple supply chain connections. However, in GSC collaboration, the extant literature offers limited analysis regarding the effect of NQP on its collaborative performance. Therefore, this paper specifically concentrates on enterprise GSC collaboration. It empirically tests the facilitative effect of enterprise NQP on enterprise GSC collaboration performance.

6. Conclusions and Enlightenments

Employing empirical methodologies and Chinese A-share listed companies’ data between 2018 and 2022, the effect of enterprises’ NQP (New Quality Productive forces) on their GSC (Green supply chain) collaboration has been explored. Our key finding indicates that enhancing an enterprise’s NQP positively affects the GSC collaboration. In addition, the moderating effect test indicated that the digitalization of an enterprise’s supply chain enhances the positive effect of NQP on GSC collaboration. Analysis of heterogeneity further demonstrated that the promotion of GSC synergy by enterprises’ NQP is more significant in NSOE, firms in high-tech industries, and those operating outside of heavily polluting sectors. Based upon these research conclusions, several key conclusions can be drawn, as detailed below.

First, it is crucial to elevate the developmental level of enterprises’ NQP to facilitate improvements in their GSC collaborative performance. In light of advancing economic globalization and the increasing degradation of the ecological environment, contemporary supply chains must extend their scope beyond mere economic gains, placing increased emphasis on environmentally conscious development. Effective coordination in enterprise supply chains not only underpins smooth operational functionality but also acts as a vital platform for inter-organizational cooperation. In effect, our research has confirmed that advancements in enterprises’ NQP exert a significant positive effect on the enhanced synergy of GSCs. Therefore, we suggest that enterprises should prioritize the cultivation of their NQP by enhancing investment in innovation, actively promoting “innovation strong chain”, and striving for synergistic advancement across both economic and environmental performance, while valuing GSC synergy. It is recommended that enterprises establish a green and sustainable development philosophy, prioritize the recruitment of innovative professionals, and increase R&D investments in green technologies. By systematically integrating data assets into operational frameworks and implementing smart supply chain solutions through technological innovation, organizations can achieve dual efficiency gains in operational performance and cost optimization. Furthermore, enterprises should proactively fulfill corporate social responsibilities while strategically aligning environmental stewardship with business objectives. This integrated approach enables companies to cultivate sustainable competitive advantages through value chain innovation, ultimately driving high-quality development characterized by ecological and economic synergies.

Second, a strengthened emphasis on the digital evolution of enterprise supply chains is essential to promote the role of NQP in cultivating GSC collaboration. Digitalizing supply chains involves converting information relevant to regulations, processes, and diverse enterprises in the chain into data, which allows for enhanced feedback and operational monitoring, improved efficiency in upstream and downstream cooperation, and finally, superior supply chain performance. This study provides evidence that the digitalization of supply chains positively contributes to fostering and reinforcing the synergistic relationship between enterprises’ NQP and GSC, aligning with existing research findings [32]. However, the present level of digitalization in Chinese enterprises requires further advancement, and supply chain digitalization remains largely in the trial stage. Therefore, enterprises are encouraged to proactively champion digital transformation across their operations and in their supply chains, thereby facilitating information exchange throughout all supply chain stages, strengthening close inter-enterprise relationships, and fully leveraging digital technologies for tracking and documenting information flow. This digital integration should promote enhanced interaction and collaboration among upstream and downstream partners, thereby solidifying the effect of NQP on GSC synergy.

Third, policymakers should strengthen institutional mechanisms to enhance corporate social responsibility (CSR) compliance among state-owned enterprises (SOEs). Targeted policy incentives should be designed to direct SOE capital toward innovation-driven initiatives, enabling these entities to assume leadership roles in green supply chain (GSC) ecosystems. For non-state-owned enterprises (NSOEs), differentiated incentive structures should be implemented to reward exemplary CSR performance, thereby stimulating innovation-oriented behaviors. Concurrently, national regulatory frameworks must institutionalize supply chain facilitation mechanisms—such as standardized sustainability certifications and collaborative R&D subsidies—to lower barriers to inter-firm cooperation. This dual approach fosters public–private complementarity in GSC advancement while aligning organizational motivations with systemic sustainable objectives. Meanwhile, market regulation and guidance mechanisms should be employed to incentivize enterprises to allocate specific financial resources towards innovation and research and development. This investment in new technology is vital for enhancing enterprise productivity levels.

The transformative influence of emerging advanced technologies on green supply chain collaboration is particularly pronounced in high-tech enterprises, owing to their technological enhancement capabilities. This necessitates a dual strategic focus: first, the cultivation of robust internal financial capacity to support technological adoption, and second, the systematic development of inter-organizational collaborative mechanisms across supply chain networks. Conversely, non-high-tech enterprises should emphasize organizational development through strategic R&D investments aimed at cultivating innovative productive capabilities. Such technological capacity building enables these firms to overcome inherent resource constraints and effectively participate in environmentally sustainable supply chain partnerships. This differential approach underscores the critical role of technological preparedness in achieving circular economy objectives across industrial sectors. Moreover, enterprises should recognize the positive effects of innovation on NQP and enhance their innovation investments to strengthen enterprise GSC synergy.

Enterprises operating in high-emission industries must prioritize direct emission reduction through technological retrofitting and strategic capital allocation toward emission mitigation infrastructure. Such operational recalibration aligns with both regulatory compliance imperatives and long-term decarbonization commitments. Conversely, low-emission enterprises should concentrate on systemic supply chain optimization by developing next-generation production paradigms—such as AI-driven resource circulation systems or blockchain-enabled transparency protocols. This strategic bifurcation enables differentiated pathways for green transition: emission-intensive sectors address immediate environmental liabilities, whereas technologically agile firms leverage their operational flexibility to orchestrate cross-tier sustainability synergies. Moreover, it is recommended that an improved evaluation system for corporate social responsibility be established and optimized to increase enterprises’ focus on social responsibility. Special attention should be directed towards the NQP of enterprises in non-heavy polluting industries, with the aim of further advancing the environmental performance of the supply chain.