New-Quality Productive Forces, Green Technological Innovation and Modernization of the Industrial Chain

Abstract

In recent years, as a new driving force for building a modern industrial system, new-quality productive forces have emerged as a key factor in advancing the high-end, intelligent, and green development of industrial chains. This study selects panel data from 31 provincial-level administrative regions in China (excluding Hong Kong, Macau, Taiwan, and the Tibet Autonomous Region) for the period 2011–2021 as the research sample. A regression analysis model is constructed from three dimensions—overall effect, moderating effect, and spatial spillover effect—to empirically examine the impact of new-quality productive forces on industrial chain modernization. The results indicate that new-quality productive forces exert a stable and significant promotional effect on industrial chain modernization and generate an indirect positive impact by driving green technological innovation.

1. Introduction

As global industrialization and urbanization proceed at an accelerated pace, challenges such as excessive greenhouse gas emissions and worsening environmental pollution have emerged as severe threats to human health, the sustainable socioeconomic development of societies, and the stability of global ecosystems. Against this backdrop, pursuing green and low-carbon development has evolved into a broadly acknowledged consensus within the international community [1]. To drive low-carbon transitions across entire industrial chains, developed countries primarily leverage legislative measures. For instance, the European Union (EU), through the European Climate Law, mandates a five-year assessment of emission reduction progress starting from 2023: it also advances energy transition and research and development (R&D) on low-carbon technologies such as hydrogen energy. The United States, via the Inflation Reduction Act, has allocated hundreds of billions of dollars to guide the reshoring of clean energy supply chains. Meanwhile, Japan revised its Basic Energy Plan, increasing funding for the Green Innovation Fund to support R&D in hydrogen technologies and carbon capture, utilization, and storage. By contrast, developing countries focus on low-carbon initiatives that integrate technology introduction with local innovation. Brazil, for example, has established social and environmental sustainability standards across multiple industries, providing certification for products and services that meet these criteria. It has also launched “green equities” to incentivize enterprises to enhance environmental investments and channel capital into green industries. India introduced the National Geothermal Energy Policy, which aims to advance R&D in hybrid geothermal–solar power plants and the retrofitting of abandoned oil wells for geothermal applications. Uzbekistan, through collaboration with China, has constructed waste-to-energy facilities—an effort that has effectively reduced carbon emissions associated with landfills. These diverse practices underscore that green and low-carbon development, as the core strategy for global sustainable development in the 21st century, holds dual critical values. On the one hand, it helps mitigate resource constraints, alleviate environmental pressures, and advance ecological conservation. On the other hand, it has gradually emerged as a key pathway to drive economic restructuring and achieve high-quality development [2].

In the development of a low-carbon economy, the industrial sector, as the core carrier of energy consumption and carbon emissions, faces an urgent need for low-carbon transformation [3]. Green technological innovation serves as the key support to address this challenge: through technological advancement, it helps enterprises reduce pollution, enhance resource efficiency, and develop eco-friendly products [4]. Covering the R&D of renewable energy technologies, energy-saving technologies, clean production processes, and eco-friendly materials, green technological innovation acts as the core driver of low-carbon economic development [5]. Against this backdrop, the rise in new-quality productive forces provides new impetus and direction for green technological development. New-quality productive forces are an important component of Xi Jinping’s economic thought and a significant innovation in China’s independent knowledge system of economics. Compared with traditional productive forces, new-quality productive forces embody a profound transformative shift. They move beyond the outdated production model defined by high energy consumption, high emissions, and low efficiency, and instead evolve into a new form of productive capacity centered on scientific and technological innovation, integrating cutting-edge technologies such as data, networks, and intelligence [6]. This transformation fundamentally changes the path of productive forces, making production activities cleaner, more efficient, and more sustainable [2]. There is an inherent unity between new-quality productive forces and green productive forces [7]. New-quality productive forces themselves are green productive forces, and green should be a necessary condition for new-quality productive forces, while new-quality productive forces should promote green development. The innovation and green development orientation of new-quality productive forces are the key support for building a modern economic system with new-quality productive forces. Specifically, green technology innovation is needed to promote the transformation of industrial chains to high-end, green and intelligent directions.

The industrial and supply chains are pivotal to the smooth operation of a nation’s economy, and the optimization and upgrading of industrial chains constitute a core component of building a modern industrial system [8]. Globally, the modernization of industrial chains has become a core area of competition among countries [9]. The European Union, through the “European Green Deal,” has designated green technological innovation as the core driver of industrial chain upgrading, aiming to raise the share of renewable energy to 42.5% by 2030 and thereby forcing industrial chains to undergo low-carbon restructuring. The United States released the “Advanced Manufacturing Leadership Strategy” report, focusing on key sectors such as semiconductors and new energy, and strengthening the autonomy and controllability of industrial chains through government subsidies and technological blockades. Emerging economies like India and Vietnam are accelerating the transfer of mid-to-low-end industrial chains, seeking to break free from the division-of-labor lock-in by leveraging factor cost advantages. Meanwhile, the restructuring of international rules, such as carbon tariffs and technology export controls, has further intensified the security and green competition in global industrial chains [10]. Against this backdrop, the modernization of China’s industrial chains not only requires addressing domestic bottlenecks in factors and technologies but also demands cultivating “green + digital” dual advantages through new-quality productive forces, thereby shifting from scale advantages to quality advantages in the global industrial chain division of labor. This is both a necessary choice to respond to international competition and an inherent requirement for achieving the “dual carbon” goals and high-quality development.

During his inspection tour in Guangdong Province, China in 2023, President Xi Jinping emphasized “promoting the advanced development of industrial foundations and the modernization of industrial chains.” Advancing the modernization of industrial chains serves as the core focus of accelerating the construction of a modern industrial system rooted in the real economy. The modernization of industrial chains can enhance China’s advantages in international industrial competition, enabling it to respond more safely and swiftly to sudden shocks and challenges in the global environment, and prevent issues such as chain breaks and missing links [11]. Nevertheless, China’s industrial chains still face pressing challenges such as limited technological development, high input with low output, poor circulation of industrial chain elements, and low resource integration capabilities [12]. The 2024 Government Work Report of China proposed accelerating the development of new-quality productive forces and vigorously promoting the construction of a modern industrial system. It emphasizes the need to fully leverage the leading role of scientific and technological innovation, speed up the optimization and upgrading of industrial and supply chains, enhance the resilience and competitiveness of industrial chains, and promote their transformation toward higher-end, smarter, and greener development. As the primary driver of industrial transformation and upgrading, new-quality productive forces provide strong support for advancing industrial foundation modernization and upgrading industrial structures through scientific and technological innovation, and the adoption of emerging, efficient, and green production methods [13]. Specifically, their role manifests in three key aspects: Firstly, new-quality productive forces take scientific and technological innovation as their core driving force. This enables them to break through technical “bottlenecks” and address “chain failures,” while optimizing industrial production processes and elevating the level of independent innovation across entire industrial chains. Secondly, new-quality productive forces foster innovative workforce models. These models not only significantly improve corporate operational efficiency but also drive organizational transformation, accelerating the industrial chains from “low-end specialization” to “high-end expansion”. Thirdly, by redefining the combination of production factor, these advanced productive forces disrupt traditional manufacturing paradigms and catalyze emerging business models. Meanwhile, they leverage the multiplier effect of data to enhance industrial chains’ capabilities in data acquisition, transmission, and processing, strengthening supply chain resilience and propelling the transition from “fragmented short chains” to “coordinated long chains”.

A review of existing literature reveals that few scholars have explored the mechanism of green technology innovation between new-quality productive forces and the modernization of China’s industrial chain. In the research field of new-quality productive forces and industrial chain modernization, some studies point out that new-quality productive forces possess empowering value for the modernization of industrial and supply chains. Specifically, new-quality productive forces drive the upgrading of industrial chains from low-end to high-end by restructuring production factors and optimizing organizational models, with technological innovation and digital transformation functioning as mediating and moderating variables in this process [14,15,16,17,18]. In the context of green technology innovation, new-quality productive forces are built on the integration and collaboration of modern green technologies as well as the innovative linkage of productivity structure elements. They facilitate the innovative development of green technologies in cultivating green production relations, transforming technological innovation achievements, and expanding international exchanges and cooperation [19]. Meanwhile, green technology innovations such as artificial intelligence, industrial internet, and new energy have accelerated the modernization of industrial chains [20,21,22].

It is evident that research on new-quality productive forces, green technological innovation, and the modernization of China’s industrial chain is relatively scarce, leaving significant room for theoretical expansion. Under the background of the “dual control” policy on energy and the “dual carbon” goals, new-quality productive forces offer unprecedented development opportunities for the construction of a modern industrial system. However, how can new-quality productive forces effectively promote the transformation and upgrading of industrial chains? What role does green technological innovation play in this process? Do new-quality productive forces have a spatial spillover effect? A systematic exploration of these questions not only enriches theoretical research on the interaction between new-quality productive forces and industrial chains but also provides practical guidance for China to achieve the modernization of its industrial chain and boost high-quality economic development, carrying both academic value and practical significance.

The innovation points and potential impacts of this study are as follows:

Key Innovations: (1) Research Framework: Existing literature primarily focuses on either the direct impact of new-quality productive forces on industrial chain modernization or the role of green technological innovation in industrial upgrading. However, few studies systematically reveal the mediating mechanisms between these two factors. This study fills the gap in the “new-quality productive forces–industrial chain modernization” intermediate mechanism by establishing a multi-path analytical framework. (2) Research Methodology: The study establishes a progressive empirical system across three dimensions—holistic effects and moderating effects. By adopting a multidimensional model design and conducting “structural disparity” analysis of spatial effects, it further enhances the rigor and reliability of the research conclusions.

Potential Impact: Against the backdrop of global supply chain turbulence and the “dual carbon” goals becoming an international consensus, the findings of this study hold international reference value. For developing countries, it provides actionable pathways to leverage green technological innovation for the coordinated advancement of industrial chain upgrades, helping them avoid the traditional pitfall of polluting first and then cleaning up. Meanwhile, it validates that green technology innovation can enhance the role of new-quality productive forces in empowering industrial chains, offering China solutions for the global industrial chains and green upgrading.

2. Mechanism Analysis and Research Hypothesis

2.1. New-Quality Productive Forces Contribute to the Modernization of the Industrial Chain

The Marxist theory of productive forces and production relations holds that their dynamic interaction constitutes the contradictory movement inherent in the mode of production. Specifically, the advancement of productive forces necessitates corresponding adjustments in production relations, while optimized production relations; conversely, fuel the development of productive forces. New-quality productive forces, with technological innovation as their core driver, represent a new stage in the evolution of productive forces. Their defining characteristic lies in the reconfiguration of production factor combinations through digital technologies, green innovations, and artificial intelligence. In contrast, industrial chain modernization focuses on optimizing production relations, particularly in production factor allocation and industrial collaboration models. The interplay between these two aspects embodies the law of productive forces and production relations. New-quality productive forces provide material foundations and technical support for industrial chain modernization, while the production relations adjustments achieved through industrial chain modernization further propel the development of new-quality productive forces.

New-quality productive forces are logically aligned with the requirements of industrial chain modernization, manifesting three key facilitative effects. First, they drive organizational transformation in industrial chains by restructuring factor collaboration. Horizontally, digital technologies dismantle physical barriers between traditional industries, enabling the free flow and recombination of production factors across sectors—exemplified by technological integration between manufacturing and services, or cross-sectoral convergence between agriculture and the digital economy. This factor reorganization compels industrial chains to shift from linear divisions of labor to networked collaborative production models. Such changes not only reduce transaction costs among stakeholders but also foster new value distribution patterns through industrial convergence, ultimately shaping industrial chains with networked and ecological organizational characteristics. Vertically, new-quality productive forces embed data elements throughout the entire chain, restructuring information transmission mechanisms between upstream and downstream sectors. This addresses factor misallocation caused by information asymmetry in traditional production relations, shifting resource allocation authority from hierarchical control to data-driven mechanisms while enhancing upstream–downstream coordination. Fundamentally, this represents a profound adjustment in the governance of production factors. Second, they drive transformations in industrial forms and value distribution by upgrading the attributes of traditional factors. Unlike conventional productive forces where factors operate in fixed proportions, new-quality productive forces empower production factors through technological innovation, endowing the means of production, objects of labor, and the workforce with new attributes. This factor upgrade not only enhances production efficiency but also drives transformative changes in the logic of resource allocation within production relations. On the one hand, the integration of data as a production factor shifts value distribution in industrial chains from the traditional model based on capital and labor contributions to one centered on data’s value-creation capacity. This transition propels industries from resource-intensive to technology-intensive models, representing the inevitable adaptation of production relations to the evolution of productivity. On the other hand, new-quality productive forces gradually replace traditional production factors, transforming industrial chains from extensive production models to green, low-carbon production patterns that achieve dual improvements in factor utilization efficiency and sustainable development capabilities. Third, new-quality productive forces strengthen industrial chain control by reconfiguring production factor allocation. Internally, they enable remote management of production factor flows via digital information systems, reducing information errors and scheduling losses while safeguarding supply chain security. By digitally transforming traditional production factors, they accelerate the integration of mid-to-low-end industrial chains, shorten supply chain length, minimize the risk of disruptions, and enhance overall stability. Externally, they drive digital and intelligent industrial innovation: while transforming traditional production models, they create new demands and products on both the demand and supply sides of industrial chains. This process fosters strategic emerging industries and future-oriented sectors, effectively mitigating external environmental impacts on industrial chains and enhancing their risk resilience. Fundamentally, this represents productivity advancements that propel production relations to adapt to higher-level competition.

Based on the above analysis, new-quality productive forces promote the modernization and upgrading of industrial chain by reconstructing the cooperative relationship of factors, upgrading the attributes of traditional factors, and reconstructing the allocation mode of means of production. Therefore, the paper puts forward the following hypotheses:

Hypothesis H1:

New-quality productive forces significantly promote industrial chain modernization.

2.2. The Moderating Role of Green Technology Innovation in New-Quality Productive Forces and Industrial Chain Modernization

In the process of new-quality productive forces driving industrial chain modernization, green technology innovation plays a pivotal role in supporting industrial chain optimization and upgrading. Through its inherent technical characteristics and functional attributes, green technology innovation modifies the intensity and scope of new-quality productive forces’ impact on industrial chain modernization. This indicates that green technology innovation may serve as a crucial moderating variable in the modernization process. The specific mechanisms can be elaborated from the following two aspects:

First, green technological innovation can enhance the technological absorption capacity of industrial chains. While new-quality productive forces can foster innovative production models and industrial forms through digital integration and technological breakthroughs, their effectiveness in driving industrial chain modernization heavily depends on the chain’s inherent technological absorption capabilities. Can enterprises, organizations, or regions within industrial chains effectively absorb advanced technological achievements from new-quality productive forces through their resource reserves and technical foundations, and transform them into tangible productive capabilities? In this process, green technological innovation plays a pivotal role. On one hand, green technological innovation is inherently technology-intensive, enabling it to address core challenges within the industrial chain such as high energy consumption, severe pollution, and excessive technological dependence. It helps break external technological monopolies, achieve self-reliance in core technologies, and clear obstacles for the digital integration of productive forces. When green technological innovation reaches advanced levels, the digital elements of new-quality productive forces can integrate more efficiently into upstream and downstream links of the industrial chain. Through network-based restructuring, this enables precise resource allocation and optimized production processes, thereby significantly advancing technological autonomy and intelligent upgrades in industrial chains. Conversely, if green technological innovation remains underdeveloped, key links in the industrial chains will be constrained by technological bottlenecks, making it difficult for the digital elements of new-quality productive forces to penetrate deeply. Also, the driving force for industrial chain modernization will be significantly weakened. On the other hand, the low-carbon substitution effect brought by green technological innovation optimizes the industrial structure foundation of the industrial chain. After eliminating outdated production capacities that are energy-intensive and low-yielding, the industrial chain becomes more receptive to emerging industries like new energy driven by new-quality productive forces, making the promotion effect of new-quality productive forces on industrial chain upgrading more prominent. In contrast, insufficient green technological innovation leads to outdated production capacities dominating the industrial chain, leaving emerging industries spawned by new-quality productive forces struggling to take root, greatly limiting their driving effect on industrial chain modernization.

Second, green technological innovation can enhance the resilience of industrial chains. The modernization of industrial chains not only requires technological upgrading and structural optimization but also demands robust capacity to withstand external risks, primarily through two pathways. Firstly, green innovation reduces dependence on high-risk resources, thereby mitigating supply chain disruption risks. The stability of industrial chains relies on continuous supplies of core resources like energy and raw materials. Traditional high-carbon industrial chains often face significant supply risks, driven by factors like fossil fuel price volatility, scarcity of critical mineral resources, and tightening environmental policy constraints. On the one hand, green innovation shifts industrial chains from fossil fuels to renewable energy through clean energy substitution, thereby reducing risks from international energy price volatility and geopolitical conflicts. On the other hand, it transforms industrial waste into raw materials through circular resource utilization, which lowers dependence on primary mineral resources and avoids supply chain disruptions caused by mining restrictions or changes in import quotas. Secondly, green innovation optimizes industrial chain structures to minimize operational risks. Low-carbon technological upgrades enable industrial chains to better comply with environmental regulations, preventing new-quality productive forces from being constrained by environmental compliance issues, and thus ensuring the sustained dynamism of new-quality productive forces in driving industrial chain modernization. Specifically, when green technological innovation reaches an advanced level, industrial chains can leverage circular economy technologies to shift from linear “extraction–production–waste” models to circular production models. This transformation not only reduces dependence on virgin resources but also mitigates resource cost risks during industrial expansion driven by new-quality productive forces. Conversely, if green technological innovation remains underdeveloped, industrial chains will continue to rely on traditional high-energy-consuming energy sources and linear production models. In such cases, industrial expansion propelled by new-quality productive forces becomes more vulnerable to energy price fluctuations and environmental regulations, leading to unstable effectiveness in industrial chain modernization and even potential temporary stagnation due to risk impacts.

Based on the above analysis, green technological innovation enhances the industrial chain’s capacity for technological adoption and risk resilience, thereby altering the intensity of new-quality productive forces’ impact on industrial chain modernization. Specifically, the higher the level of green technological innovation, the stronger the driving force of new-quality productive forces in advancing industrial chain modernization.

Hypothesis H2:

Green technology innovation plays a positive moderating role in the relationship between new-quality productive forces and industrial chain modernization. The higher the level of green technology innovation, the more significant the promoting effect of new-quality productive forces on industrial chain modernization.

Based on the above theoretical analysis, the mechanism of new-quality productive forces promoting the modernization of industrial chain through green technology innovation is shown in Figure 1.

3. Research Design

3.1. Model Building

To thoroughly analyze the impact and mechanisms of new-quality productive forces on China’s industrial chain modernization, this study develops a regression model covering three dimensions: overall effect, moderating effect, and spatial spillover effect. Firstly, to examine the fundamental influence of new-quality productive forces on industrial chain modernization, we establish the following two-way fixed-effects model:

$${\mathit{ind}\text{\_}\mathrm{m}\mathit{odern}}_{it}={\theta }_0+{\theta }_1{\mathit{new}}_{-}{\mathit{prod}}_{it}+\sum _k{\theta }_k{X}_{kit}+a_i+{\delta }_t+{\stackrel{`}{O}}_{\mathit{it}}$$

(1)

Here, it represents the modernization degree of the industrial chain in province i in year t, namely the new-quality productive forces, which represents a series of control variables selected in this paper (including human capital, economic development, social consumption, government intervention and opening to the outside world), and, respectively, individual and time fixed effects, and the disturbance term .${ind\text{\_}\mathrm{m}odern}_{\mathit{it}}{new\text{\_}prod}_{\mathit{it}}{X}_{\mathit{kit}}{a}_i{\delta }_t{\stackrel{`}{O}}_{\mathit{it}}$.

Secondly, considering that the impact of new-quality productive forces on industrial chain modernization may be affected by specific moderating factors, this study introduces interaction terms on the basis of the benchmark model to construct a moderating effect model. The specific form is as follows:

$${ind\text{\_}\mathrm{m}odern}_{it}={\eta }_0+{\eta }_1{newprod}_{it}+{\eta }_2{\mathrm{t}\mathrm{e}\mathrm{c}\mathrm{h}}_{it}+{\eta }_3\left({new}_{-}{prod}_{it}\times {\mathrm{t}\mathrm{e}\mathrm{c}\mathrm{h}}_{it}\right)+\sum _{ı}{\eta }_k{X}_{kit}+a_i+{\delta }_t+{\stackrel{`}{O}}_{\mathit{it}}$$

(2)

Among them, the interaction term of green technology innovation, which represents the moderating variables selected in this study that may affect the relationship between the two, is used to measure the intensity of the moderating effect. If the coefficient is significant, it indicates that the moderating variable will significantly adjust the intensity of the impact of new-quality productive forces on the modernization of the industrial chain ${\mathrm{t}\mathrm{e}\mathrm{c}\mathrm{h}}_{it}{newprod}_{it}\times {\mathrm{t}\mathrm{e}\mathrm{c}\mathrm{h}}_{it}{\eta }_3$.

Finally, this paper further introduces the Spatial Durbin Regression Model (SDM) to identify spatial spillover effects.

$${ind\text{\_}\mathrm{m}odern}_{it}={\xi }_0+{\xi }_1{new\text{\_}prod}_{it}+{\xi }_{2}Wne{w}_{-}{prod}_{it}+\sum _k{\xi }_k{X}_{kit}+\sum _k{\xi }_{k}W{X}_{kit}+a_i+{\delta }_t+{\stackrel{`}{O}}_{it}$$

(3)

Among them, the spatial weight matrix and the spatial lag values of new-quality productive forces and control variables, respectively, are used to describe the local effect and the indirect influence of neighboring provinces $\begin{array}{cc}W& Wnew\text{\_}prod\end{array}$.

3.2. Variable Selection

3.2.1. Dependent Variable

The modernization level of the industrial chain. This study adopts the calculation method proposed by Zhang H [23]. A comprehensive indicator system is constructed from six dimensions: industrial chain foundation, industrial chain digitalization, industrial chain innovation, industrial chain resilience, industrial chain coordination, and industrial chain sustainability, and is measured using the entropy method. This system comprehensively reflects a region’s overall performance in industrial chain security, technological level, and value creation capacity. It has been widely applied in relevant research in China, showing high operability and representativeness. The specific indicator system is presented in

Table 1. Evaluation index system of industrial chain modernization level in China.

Primary Indicator	Secondary Indicator	Third-Level Indicator
Industrial chain foundation	negotiability	Total length of grade roads per 100 km2
	negotiability	Railway mileage per 100 km2
	negotiability	turnover of freight traffic
Industrial chain digitalization	Communication support	Internet penetration rate per 10,000 people
	Communication support	Length of fixed long-distance optical cable lines per 10,000 people
	Enterprise Digitalization	Number of computer workstations per 100 people
	Industrial digitalization	The proportion of electronic information manufacturing’s main business revenue in GDP
Industrial chain innovation	Innovation investment	The proportion of R&D personnel in industrial enterprises above designated size in the employed population
	Innovation Output	PCT International Patent Grant Count
	High-end leadership	The proportion of main business revenue of strategic emerging industries in the manufacturing industry
Industrial chain resilience	Chain control	Number of top 100 multinational corporations
	Chain control	The number of China’s top 500 manufacturing enterprises
	Chain control	Number of China’s most valuable brands
Industrial chain coordination	profitability	The cost per 100 yuan of operating income of industrial enterprises above designated size
	Financial Collaboration	The ratio of total loans to GDP in banking institutions
	Collaborative Innovation	Manufacturing and producer services synergy coefficient EG index
Industrial chain sustainability	Energy saving production	Energy consumption per unit of industrial output value
	pollutant discharge	Industrial unit added value sulfur dioxide emissions
	Green Governance	The proportion of completed investment in industrial pollution control projects in this year’s industrial added value

.

3.2.2. Core Explanatory Variables

New Productivity Development Level (new_prod). Drawing on Lu Jiang’s [24] research, this study defines new productivity as an advanced form of productive forces characterized by technological innovation, high technological content, high efficiency, and high quality. It covers three primary dimensions: technological productivity, green productivity, and digital productivity. The study selects 18 indicators and adopts an improved entropy-weighted TOPSIS method for weighted measurement. The results are used to characterize the development process of new productivity regarding regional economic innovation potential and production efficiency (

Table 2. New-quality productive forces index system.

Primary Indicator	Secondary Indicator	Third-Level Indicator	Account Form	Unit	Attribute
Technological productivity	Boosting productivity	Innovative R&D	Number of domestic patent grants	individual	+
		Innovation industry	High-tech industry revenue	ten thousand yuan	+
		Innovative products	Innovation funds of industrial enterprises above designated size	ten thousand yuan	+
	Technological productivity	technical efficiency	Labor productivity of industrial enterprises above designated size	%	+
		Technology	Equivalent R&D personnel in all large-scale industrial enterprises	h	+
		Technical production	Original value ratio of electromechanical equipment	%	+
Green productivity	Resource-saving productivity	Energy intensity	Energy consumption/GDP	%	−
		Energy structure	Fossil energy consumption/GDP	%	−
	Environmentally friendly productivity	Water intensity	Industrial water use/GDP	%	−
		Salvage	Comprehensive utilization rate of industrial solid waste/quantity of industrial solid waste	%	−
		Wastewater discharge	Industrial effluent discharge/GDP	%	−
		Exhaust emission	Industrial SO2 emissions/GDP	%	−
Digital productivity	Industrial digital productivity	Electronic Information manufacturing	Integrated circuit production	individual	+
		Telecommunications service	Total telecom traffic	ten thousand yuan	+
		Network broadband	Number of Internet broadband access ports	individual	+
		Software maintenance	Revenue from software services	ten thousand yuan	+
		Data Information	Optical cable line length/area	m	+
		Electronic Commerce	E-commerce sales	ten thousand yuan	+

).

3.2.3. Control Variables

To mitigate omitted variable bias, this study incorporates several control variables into the regression model: (1) Human capital level (human): As a fundamental driver of new-quality productive forces and a key factor in industrial chain upgrading, human capital accumulation is measured by the proportion of enrolled students in regional higher education institutions. (2) Economic development level (economy): According to Rostow’s economic growth stages theory, a mature economy transitions its industrial structure from traditional manufacturing to high-tech industries. This transformation requires per capita GDP to reach a critical threshold, enabling regions to allocate more resources to innovation beyond basic production needs. The economic foundation determines the material conditions and innovation capacity for industrial chain modernization, typically measured by the logarithm of regional GDP per capita. This metric eliminates population size disparities, accurately reflecting “the material wealth creation capacity per capita” and avoiding the scale and efficiency distortions inherent in total GDP measurements. (3) Social consumption level (consume): Marx’s dialectical relationship between production and consumption indicates that production not only supplies goods for consumption but also shapes consumption patterns, thereby reproducing consumption demand and momentum. Conversely, consumption influences production as it serves as a prerequisite for production and generates its driving force. Social consumption acts as a vital catalyst for industrial upgrading and domestic demand stimulation, while also serving as a key indicator of residents’ quality of life. This study employs the proportion of total retail sales of consumer goods in GDP as a proxy variable. (4) Government Intervention (government): According to Keynesian state intervention theory, market mechanisms often fail in areas such as public goods supply and industrial security assurance, necessitating government regulation through fiscal and policy measures. Government intervention plays a crucial role in regulating industrial chain security, guiding emerging industries, and ensuring industrial chain stability. This study measures government intervention using the proportion of local fiscal expenditure in GDP. (5) Degree of Openness (foreign): Based on the technology spillover effect in Krugman’s New Trade Theory, opening up allows local enterprises to absorb advanced technologies and management expertise through imitation and collaboration when importing high-end equipment and attracting foreign-funded enterprises, thereby promoting technological upgrading in industrial chains. This study selects the proportion of total import and export volume in GDP at the location of business units to represent regional openness levels.

3.2.4. Adjustment Variables

To explore the mechanisms through which new-quality productive forces influence industrial chain modernization, this study introduces green technology innovation as a moderating variable. It aims to examine its potential moderating effect in the relationship between new-quality productive forces and industrial chain modernization, specifically measured by the logarithm of annual green technology patent applications in each province. Green technology innovation not only reflects the current demand for low-carbon, eco-friendly, and efficient technologies in industrial development, but also prominently demonstrates a region’s capacity to absorb and transform technological elements during its green transition.

3.3. Sample Selection and Data Sources

Considering data availability, this study selected panel data from 31 provinces of China (excluding Hong Kong, Macao, Taiwan, and Tibet Autonomous Region) from the period 2011–2021 as the research sample. Missing data were supplemented via linear interpolation, yielding 330 observations. Variable data are mainly sourced from the China Statistical Yearbook (

Table 3. Descriptive Statistical Analysis.

Variable	N	Mean	SD	Min	Max	p50
Modernization of industrial chain	330	0.189	0.0838	0.0462	0.483	0.175
new-quality productive forces	330	0.200	0.178	0.0300	0.830	0.150
human capital	330	0.0206	0.00620	0.00852	0.0439	0.0197
economic development	330	10.83	0.451	9.682	12.14	10.79
social consumption	330	0.396	0.0624	0.240	0.576	0.399
Degree of government intervention	330	0.260	0.112	0.121	0.717	0.233
Degree of openness	330	0.271	0.280	0.0175	1.294	0.145

).

4. Empirical Analysis

4.1. Benchmark Regression Analysis

To comprehensively examine the impact of new-quality productive forces on industrial chain modernization, this study adopts a two-way fixed effects model for analysis, with regression results presented in

Table 4. Benchmark regression results.

Variables	(1)	(2)	(3)
	r1	r2	r3
	ind_modern	ind_modern	ind_modern
New-quality productive forces	0.3240 ***	0.2447 ***	0.1765 ***
	(0.026)	(0.036)	(0.041)
Human capital		0.4359 *	−0.1231
		(0.254)	(0.131)
Economic development		0.1336 ***	−0.0363
		(0.008)	(0.023)
Social consumption		0.1156 ***	0.0627 ***
		(0.038)	(0.021)
Degree of government intervention		0.1206 ***	−0.0354
		(0.025)	(0.048)
Degree of openness		−0.0798 ***	−0.2050 ***
		(0.016)	(0.021)
Constant term	0.1245 ***	−1.3716 ***	0.5898 **
	(0.005)	(0.085)	(0.249)
Time fixed effect	NO	NO	YES
Province fixed effects	NO	NO	YES
Observations	330	330	330
R-squared	0.467	0.757	0.965

Note: Values in parentheses are t-values, with *** indicating p < 0.01, ** p < 0.05, and * p < 0.1. The same notation applies throughout the text.

. After progressively introducing various control variables and fixed effects, the regression coefficient of the core variable “new-quality productive forces” consistently remains positive at the 1% significance level, indicating robust coefficient estimates. This confirms that new-quality productive forces have become a pivotal endogenous driver propelling industrial chains toward high-end, intelligent, and green development, serving as a crucial new growth engine for China’s industrial chain modernization. The positive influence of new-quality productive forces on industrial chain modernization manifests in enhancing overall technological capabilities and resource allocation efficiency, as well as reshaping production methods and organizational structures to facilitate coordinated structural and functional evolution of industrial chains. These findings align closely with China’s current “high-quality development” strategy and policy orientation for building a modern industrial system. In recent years, China has accelerated the development of new-quality productive forces represented by digital technologies, green technologies, and advanced manufacturing. By optimizing innovation resource allocation, promoting technology commercialization, and advancing the transformation and upgrading of traditional industries, the country has gradually established a modern industrial chain system centered on technological innovation. The empirical results of this study validate the feasibility of this policy approach at the data level. Rationality and effectiveness. Thus, hypothesis H1 is validated.

The regression results of control variables indicate that the coefficient of human capital level failed to pass the statistical significance test and showed a negative direction. This indicates that despite remarkable progress in China’s education popularization and higher education resource expansion, its promoting effect on industrial chain modernization has not been fully realized. A time lag may exist between the improvement of human capital quality and its transformation into industrial efficiency. Meanwhile, significant regional disparities in high-level talent attraction, skilled labor supply, and educational resource allocation may weaken the overall statistical significance of the regression effect. The economic development level exerted a significantly positive promoting effect on industrial chain modernization before introducing regional and time two-way fixed effects. This aligns with practical expectations: relatively developed regions possess stronger capabilities in capital accumulation, R&D investment, and infrastructure construction, which can provide solid support for industrial chain transformation and upgrading. However, the significance of this variable disappeared after introducing two-way fixed effects, suggesting that the positive impact of economic development mainly stems from long-term inter-regional structural differences rather than short-term intra-regional changes during the observation period. Social consumption level consistently showed significant positive effects across multiple models, indicating that the improvement of residents’ consumption capacity plays a significant role in promoting industrial chain modernization. With the continuous expansion of domestic demand and upgrading of consumption structures, enterprises are driven to accelerate product technology iteration and service model innovation, thereby realizing the optimization and restructuring of industrial chains under demand-side stimulation. The coefficient of government intervention remained significantly positive even without introducing two-way fixed effects, indicating that under the Chinese institutional environment, fiscal investment and policy support play a positive role in ensuring industrial chain security, guiding resource allocation, and promoting the development of key industries. However, this effect weakens when dual fixed effects are introduced. Finally, it is noteworthy that the coefficient of openness to the outside world shows a significant negative trend in multiple models, which may be closely related to recent macro backgrounds, including the complex and volatile international environment, frequent trade frictions, and rising supply chain security risks. In the current environment, excessive reliance on global markets may increase vulnerability to external shocks, thereby weakening the self-reliance and control capabilities of industrial chains in some Chinese regions and affecting their modernization process.

4.2. Robustness Test

To further verify the robustness of the core conclusions, this paper adopts three methods for robustness testing based on the benchmark regression, with the regression results shown in

Table 5. Robustness results.

	(1)	(2)	(3)
	Redo Measurement	Tail Trim	Delayed by One Period
New-quality productive forces	0.1144 ***	0.1595 ***	0.1575 ***
	(0.030)	(0.039)	(0.040)
Human capital	−0.0610	−0.4083	−0.4068
	(0.130)	(0.372)	(0.358)
Economic development	−0.0249	−0.0306	−0.0108
	(0.021)	(0.024)	(0.023)
Social consumption	0.0316	0.0628 ***	0.0334
	(0.022)	(0.021)	(0.021)
Degree of government intervention	−0.0906 *	−0.0503	−0.0057
	(0.051)	(0.046)	(0.045)
Degree of openness	−0.1608 ***	−0.2129 ***	−0.2005 ***
	(0.019)	(0.022)	(0.024)
Constant term	0.5171 **	0.5433 **	0.3356
	(0.233)	(0.261)	(0.245)
Time fixed effect	YES	YES	YES
Province fixed effects	YES	YES	YES
Observations	330	330	300
R-squared	0.960	0.968	0.969

Note: Values in parentheses are t-values, with *** indicating p < 0.01, ** p < 0.05, and * p < 0.1.

. First, column (1) shows that the modernization level of the industrial chain is re-measured using entropy-weighted TOPSIS on the basis of the benchmark measurement. The results indicate that the promoting effect of new-quality productive forces on the modernization level of the industrial chain remains significant, indicating the core conclusions are robust under different measurement criteria. Second, column (2) excludes extreme samples from the sample and applies a 1% tail trimming to eliminate the interference of individual outliers on the results. The regression results reveal the coefficient of new-quality productive forces remains significantly positive, confirming that extreme values have limited impact on empirical conclusions and verifying the robustness and reliability of the estimation results. Third, column (3) introduces the lagged expectation value of the new-quality productive forces variable into the benchmark model to mitigate potential endogeneity issues and examine its dynamic impact. The regression results show the one-period-lagged new-quality productive forces still exert a significantly positive effect on industrial chain modernization at the 1% significance level. This indicates that the promoting effect of new-quality productive forces is not only effective in the current period but also exhibits certain persistence and extension over time. The result aligns with the cumulative, innovative, and diffusion-lag characteristics of China’s new-quality productive forces, fully confirming the long-term supporting role of technological progress and structural optimization in industrial chain modernization.

5. Expand Analysis

5.1. Adjustment Effect Analysis

To explore the moderating role of green technology innovation in the modernization process of industrial chains influenced by new-quality productive forces, this study constructs interaction terms based on previous analysis and employs a two-way fixed effects model for estimation, with results presented in

Table 6. Adjustment effect.

	(1)	(2)
New-quality productive forces	−0.6711 ***	−0.4685 ***
	(0.097)	(0.087)
Green Technology Innovation	0.5171 **	0.5433 **
	(0.233)	(0.261)
New-quality productive forces × Green technology innovation	0.5171 **	0.5433 **
	(0.233)	(0.261)
Human capital		0.1624
		(0.321)
Economic development		−0.0216
		(0.021)
Social consumption		0.0357 *
		(0.019)
Degree of government intervention		−0.0813 **
		(0.041)
Degree of openness		−0.1549 ***
		(0.020)
Constant term	0.2643 ***	0.5405 **
	(0.039)	(0.224)
Time fixed effect	YES	YES
Province fixed effects	YES	YES
Observations	330	330
R-squared	0.966	0.976

Note: Values in parentheses are t-values, with *** indicating p < 0.01, ** p < 0.05, and * p < 0.1.

. The interaction term coefficients between new-quality productive forces and green technology innovation are positive in both columns (0.5171 and 0.5433, respectively) and significant at the 1% level. This indicates green technology innovation exerts a significant positive moderating effect. Specifically, it amplifies the marginal effects of new-quality productive forces, mitigates initial shocks or institutional frictions, and enhances its effectiveness in industrial chain upgrading and resource allocation efficiency. In other words, a high level of green technology innovation enables new-quality productive forces to fully unleash transformation momentum through green technology support. This confirms the synergistic effect of green new-quality productive forces in promoting high-quality development, which aligns with the practical logic of coordinated industrial structure and technological system transformation under China’s current “dual carbon” goals. However, the coefficients of new-quality productive forces alone are significantly negative at the 1% level. This suggests that without considering the interaction of green technology innovation, simply enhancing the level of new-quality productive forces may not contribute to the modernization of industrial chains. While green technology innovation exerts a direct positive driving effect, it may even generate short-term inhibitory effects. This phenomenon likely stems from mismatch issues during the transformation of new-quality productive forces. For instance, the introduction of new technologies disrupts traditional industrial models, or insufficient supporting resources like capital and talent result in low actual conversion rates, thereby affecting the efficiency improvement of industrial chains. The coefficients of green technology innovation in both regression analyses are positive and statistically significant at the 1% level, indicating its strong direct impetus for industrial chain modernization. As a production factor integrating ecological and technological value, green technology not only drives enterprises’ green transformation and optimizes energy structures, but also enhances inter-segment collaborative efficiency in industrial chains while promoting long-term sustainability.

In conclusion, the regression results in Table 6 verify the key role of green technology innovation as a moderating variable in promoting the modernization of industrial chain. It not only has a significant positive impact on its own, but also can effectively enhance the mechanism of new-quality productive forces.

5.2. Spatial Spillover Effect

5.2.1. Model Recognition

Before conducting spatial effect analysis, this study employed methods including the Global Moran’s I Index, Hausman test, LR test, and LM test to validate the rationality and model selection of the constructed spatial panel model. The empirical results reveal a significant positive spatial correlation between new-quality productive forces and industrial chain modernization, with the fixed effects specification being more suitable. Additionally, both the LM test and LR test showed significance at the 1% level, further indicating that among various spatial panel models, the Spatial Durbin Model (SDM) can more accurately describe regional interactions and spatial spillover effects.

5.2.2. Model Regression

The coefficient analysis of the core variable “new-quality productive forces” (new_prod) is consistently positive across all fixed effects models, with statistical significance at the 1% level. This confirms that new-quality productive forces exert a stable positive driving effect on regional industrial chain modernization. Specifically, in the time-only control model (Column 1) and bidirectional fixed effects model (Column 3), coefficients of 0.216 and 0.342, respectively, indicate that new-quality productive forces, as a novel factor input, effectively promote industrial chain upgrading toward high-end, green, and intelligent development. Secondly, spatial lag terms (W*new_prod) show significant variations across models. In the Time model, the coefficient of 0.342 remains statistically significant at the 1% level, suggesting that the improvement of new-quality productive forces in neighboring regions can significantly drive industrial chain modernization through spatial spillover mechanisms. This reflects strong positive inter-regional linkage effects, likely achieved via knowledge spillover, technology diffusion, and innovation resource sharing, demonstrating significant positive spatial externalities. In contrast, spatial lag terms in Columns (2) and (3) lose statistical significance with negative coefficients. This discrepancy suggests spatial spillover effects primarily stem from inter-regional horizontal cross-sectional differences rather than dynamic temporal variations. Incorporating individual fixed effects absorbs inter-regional structural characteristics, weakening spatial effects. This indirectly indicates China’s new-quality productive forces exhibit significant spatial structural imbalances, with large disparities in development foundations and innovation capabilities across regions, leading to insufficient inter-regional coordination. Meanwhile, the rho value reflects the autocorrelation of spatial errors. In the Time model, rho equals −0.303, a significantly negative value, suggesting that residual terms still demonstrate negative spatial correlation after controlling for spatial lag variables. Additionally, the Time model achieves an R² of 0.787 in model fit index, markedly higher than the other two models. This demonstrates that retaining regional differences while considering temporal effects better captures the authentic impact pathways of new-quality productive forces.

In conclusion, after a comprehensive evaluation of factors including variable significance, spatial effect identification capability, and model explanatory power, this paper concludes that the spatial Durbin model with only time fixed effects (Time) is the most suitable for analyzing the spatial spillover effects of new-quality productive forces (

Table 7. Spatial regression results.

	Time	Ind	Both
New-quality productive forces	0.216 ***	0.184 ***	0.342 ***
	(0.0149)	(0.0283)	(0.0924)
W*new-quality productive forces	0.342 ***	−0.0737	−0.108
	(0.0924)	(0.131)	(0.192)
Rho	−0.303 *	0.738 ***	−0.127
	(0.164)	(0.0517)	(0.168)
Controlled variable	YES	YES	YES
Time fixed effect	YES	YES	YES
Province fixed effects	YES	YES	YES
Observations	330	330	330
R-squared	0.787	0.077	0.028
Log-likelihood	728.3469	877.7003	904.4739

Note: Values in parentheses are t-values, with *** indicating p < 0.01 and * p < 0.1.

).

To further validate the robustness of spatial regression results and avoid model bias caused by differences in weight matrix settings, this study replaces the previously used economic geography nested matrix with a spatial economic distance matrix (constructed as an inverse distance matrix based on per capita GDP differences in the base period) to conduct comparative estimation of spatial effects of new qualitative productivity. The results are presented in

Table 8. Robustness test of spatial regression.

	Time	Ind	Both
New-quality productive forces	0.204 ***	0.183 ***	0.138 ***
	(0.0146)	(0.0289)	(0.0290)
W*new-quality productive forces	0.0761	−0.192 *	−0.310 ***
	(0.0575)	(0.0988)	(0.118)
Rho	0.00443	0.704 ***	−0.00428
	(0.0992)	(0.0482)	(0.102)
Controlled variable	YES	YES	YES
Time fixed effect	YES	YES	YES
Province fixed effects	YES	YES	YES
Observations	330	330	330
R-squared	0.795	0.060	0.207
Log-likelihood	713.5179	861.8055	910.7452

Note: Values in parentheses are t-values, with *** indicating p < 0.01 and * p < 0.1.

. The findings indicate that local effects (new qualitative productivity) remain significantly positive across all three fixed effect models, with core coefficients of 0.204, 0.183, and 0.138 for Time, Ind, and Both models, respectively, all statistically significant at the 1% level. This confirms the stable and robust direct driving effect of new-quality productive forces on local industrial chain modernization, demonstrating universal applicability and robustness regardless of spatial matrix configurations. In contrast, the spatial lag term (W*new_prod) shows significant variations across models. In the Time model, the coefficient of 0.0761 for the spatial lag term remains positive but lacks statistical significance, suggesting that economic similarity-driven spatial diffusion mechanisms still exhibit positive externalities when provincial fixed effects are not absorbed. However, introducing provincial fixed effects results in a significant negative coefficient, indicating that after controlling for cross-sectional heterogeneity, the high-level new-quality productive forces in neighboring regions may paradoxically create “technological suction” or “innovation competition” effects, inhibiting the coordinated evolution of regional industrial chains. This reversal from positive to negative trends aligns closely with the findings based on the nested matrix model mentioned earlier. Moreover, the goodness-of-fit metrics indicate that the log-likelihood values for all three parameter configurations are slightly lower than those of the nested matrix model (see Table 7), with R² values also showing an overall downward trend. Statistically, this indicates that the nested matrix model provides a more comprehensive explanation of the origins and mechanisms of spatial linkages compared to the single economic distance matrix. In summary, the economic geography nested matrix selected in this study exhibits strong robustness.

5.3. Heterogeneity Analysis

The baseline regression analysis demonstrates that new-quality productive forces (new_prod) exert a robust positive driving effect on industrial chain modernization (ind_modern). Robustness tests further confirm the conclusion’s validity across alternative measurement methods, tail-trimming treatments, and dynamic modeling scenarios. Spatial expansion analysis reveals that while new-quality productive forces exhibit significant positive spatial spillover effects when controlling only for temporal effects, the spillover effects weaken in neighboring regions after incorporating provincial fixed effects. This indicates that the spatial spillover effects of new-quality productive forces primarily stem from inter-regional structural disparities rather than short-term intra-provincial fluctuations over time.

Based on this, we conducted a heterogeneity analysis of the macro-regional divisions between East, Central, and West. The study grouped regions according to three common classifications: East (11 provinces/municipalities), Central (8 provinces/municipalities), and West (12 provinces/municipalities), with the regression results presented in

Table 9. Group Regression Results.

Variables	(1)	(2)	(3)
	East	Centre	West
	ind_modern	ind_modern	ind_modern
New-quality productive forces	0.4325 ***	0.0604 **	0.0309
	(0.062)	(0.028)	(0.054)
Constant term	1.5726 **	0.1712	0.2669
	(0.722)	(0.434)	(0.263)
Controlled variable	YES	YES	YES
Time fixed effect	YES	YES	YES
Province fixed effects	YES	YES	YES
Observations	121	88	121
R-squared	0.971	0.965	0.971

Note: Values in parentheses are t-values, with *** indicating p < 0.01 and ** p < 0.05.

.

The coefficient of new-quality productive forces in the eastern region is 0.4325 (1% significant), significantly higher than the 0.1765 in the full-sample benchmark model. This indicates the eastern region has the highest conversion efficiency of new-quality productive forces. The central regions’ coefficient is 0.0604 (5% significant), suggesting that the absorption and diffusion of new-quality productive forces in the central region face thresholds and frictions, manifesting more as gradual improvements rather than leapfrog upgrades. The western regions coefficient of 0.0309 (insignificant) indicates that new-quality productive forces have yet to form a systematic driving force for industrial chain modernization.

These findings align with the earlier observation that spatial spillover effects significantly weakened after controlling for provincial effects, further confirming that regional structural disparities constitute the critical context influencing the intensity of new-quality productive forces’ impact. The primary reason for this outcome lies in the fact that the “modernization of industrial chains” indicator system in this study encompasses six dimensions—innovation, digitalization, collaboration, and sustainability—measuring the comprehensive transition from technological advancement to organizational restructuring and ecological transformation. Eastern regions hold systematic advantages in forming industrialization closed loops, market-oriented interfaces, and supporting productive service industries. These advantages enable more efficient conversion of new-quality productive forces into industrial chain structural upgrades, as reflected by higher marginal coefficients. In contrast, enterprises in central and western regions face capability gaps in process optimization, data governance, scenario engineering, and system integration. These shortcomings hinder the timely transformation of new-quality productive forces into improvements in industrial chain efficiency and resilience, ultimately resulting in lower marginal productivity.

6. Discussion

This study elucidates the moderating role of green technology innovation in driving industrial chain modernization through new-quality productive forces. The research findings will be discussed in the following section.

Firstly, the new-quality productive forces exert a significant positive driving effect on industrial chain modernization. Empirical results show that the positive driving effect of new-quality productive forces on industrial chain modernization remains statistically significant at the 1% level in both the benchmark regression and three robustness tests, confirming its reliability as a core driving factor for industrial chain upgrading. However, the results of control variables present contradictions worthy of further discussion. On one hand, the coefficient failed the significance test and was negative, contradicting the traditional understanding that human capital supports industrial upgrading. This does not mean human capital is insignificant; instead, it reflects dual issues of quality mismatch and conversion lag in China’s human capital. The expansion of human capital scale brought by the enrollment expansion in higher education institutions has not yet fully matched the precise demand for digital skills and green technology capabilities in industrial chain modernization. The digital transformation of manufacturing requires versatile data talents, while traditional human capital cultivation focuses more on single technologies or basic skills. Meanwhile, transforming human capital into industrial efficiency requires school-enterprise collaborative training, but the current mechanism leads to a long transformation cycle, resulting in the absence of a significant positive effect in empirical results. On the other hand, the coefficient of openness to the outside world was significantly negative, which had a distinct realistic background when combined with the current international environment. Currently, the turbulence in global supply chains, escalating trade frictions, and tightened technology export controls are exposing over-reliant industrial chains to risks of supply chain disruptions and technological blockades. Meanwhile, the inertia of transferring low-end production capacities during opening-up may slow industrial chains’ transformation toward high-end development, ultimately making short-term risks from openness outweigh long-term spillover effects. In the modernization of industrial chains, cultivating new-quality productive forces should be the core focus, prioritizing three dimensions: digital productivity, technological productivity, and green productivity. This approach avoids the pitfalls of blindly pursuing scale expansion and emphasizes the efficiency of technology transfer and factor reorganization.

Secondly, the moderating effect analysis reveals that the interaction term coefficient between green technology innovation and new-quality productive forces is significantly positive at the 1% level. Additionally, green technology innovation alone exerts a significant positive effect on industrial chain modernization. This indicates that green technology innovation can enhance the catalytic role of new-quality productive forces in industrial chain modernization. By improving the industrial chain’s technological adoption capacity and risk resilience, green technology innovation amplifies the marginal effects of new-quality productive forces. When green technology innovation reaches advanced levels, industrial chains can rapidly adapt to digital elements in new-quality productive forces through clean energy substitution and circular resource utilization technologies, avoiding the dilemma of unimplemented technological introductions. Meanwhile, green technology innovation reduces industrial chains’ dependence on fossil fuels and scarce minerals, mitigating resource supply risks during new-quality productive forces-driven industrial expansion and ensuring more sustainable positive effects. To avoid the misconception of prioritizing new-quality productive forces scale over green technology adaptation, green technology innovation should be integrated throughout the cultivation process of new-quality productive forces. This approach focuses on addressing the disconnect between technological R&D and industrial application, preventing resource misallocation during the transformation of new-quality productive forces.

Thirdly, the spatial Durbin model and the analysis of heterogeneity in eastern, central, and western regions jointly reveal that the role of new-quality productive forces exhibits regional imbalance characteristics. When only the time effect is controlled, new-quality productive forces show significant positive spatial spillover effects. However, after introducing provincial fixed effects, the spillover effect disappears and the coefficient turns negative. In the heterogeneity regression, the coefficient of new-quality productive forces in the eastern region is significantly higher than that in the central region, while the western region’s coefficient is statistically insignificant. The core reason for this result lies in the constraints imposed by regional structural differences on the role of new-quality productive forces. From the root cause of regional heterogeneity, due to the better economic development in China’s eastern region, it can form a coordinated industrial chain from R&D to application, enabling the rapid transformation of new-quality productive forces from technological upgrading to value creation. However, enterprises in the central and western regions face shortcomings such as insufficient low-carbon transformation capabilities in traditional manufacturing and weak digital foundations in small and medium-sized enterprises. These hinder the implementation of green technologies in specific production scenarios, making it difficult for new-quality productive forces to translate into actual industrial chain efficiency, ultimately manifested as small or insignificant coefficients. In the process of industrial chain development, it is necessary to abandon the “one-size-fits-all” development policies and design differentiated new-quality productive forces coordination mechanisms for the different development stages of the eastern, central, and western regions, addressing the dilemma of insufficient spillover effects in the east and weak reception capacity in the central and western regions.

7. Conclusions

Using panel data from 31 provincial-level administrative regions in China (excluding Hong Kong, Macao, Taiwan, and the Tibet Autonomous Region) spanning 2011–2021, this study systematically investigates the mechanism through which new-quality productive forces influence industrial chain modernization, from three dimensions: overall effect, moderating effect, and spatial spillover effect. The key findings are as follows: 1. New-quality productive forces exerts a robust promotional impact on the advancement of industrial chain modernization. 2. Green technology innovation functions as a moderator in this relationship, effectively amplifying the driving effect of new-quality productive forces on industrial chain modernization. 3. New-quality productive forces exhibit a spatial spillover effect, yet this effect remains uneven, primarily attributable to disparities in regional industrial structure.

Drawing on the above findings, it is recommended that the Chinese government tailor the development of new-quality productive forces to regional characteristics, elevate the level of green technology innovation, and thereby accelerate the modernization and upgrading of industrial chains.