Empowering the Intelligent Transformation of the Manufacturing Sector Through New Quality Productive Forces: Value Implications, Theoretical Analysis, and Empirical Examination

Abstract

Achieving sustainable development goals remains a core issue in global development. In response, China has proposed the development of new quality productive forces (NQPFs) through innovative thinking, emphasizing that fostering NQPFs is both an intrinsic requirement and a pivotal focus for advancing high-quality development. Concurrently, the intelligent transformation of the manufacturing sector serves as a critical direction for China’s economic restructuring and upgrading. This paper places “new quality productive forces” and “intelligent transformation of manufacturing” within the same analytical framework. Starting from the logical chain of “new quality productive forces—three major mechanisms—intelligent transformation of manufacturing,” it concretizes the value implications of new quality productive forces into a systematic conceptual framework driven by the synergistic interaction of three major mechanisms: the mechanism of revolutionary technological breakthroughs, the mechanism of innovative allocation of production factors, and the mechanism of deep industrial transformation and upgrading. This study constructs a “3322” evaluation index system for NQPFs, based on three formative processes, three driving forces, two supporting systems, and two-dimensional characteristics. Simultaneously, it builds an evaluation index system for the intelligent transformation of manufacturing, encompassing intelligent technology, intelligent applications, and intelligent benefits. Using national time-series data from 2012 to 2023, this study assesses the development levels of both NQPFs and the intelligent transformation of manufacturing during this period. The study further analyzes the impact of NQPFs on the intelligent transformation of the manufacturing sector. The research results indicate the following: (1) NQPFs drive the intelligent transformation of the manufacturing industry through the three mechanisms of innovative allocation of production factors, revolutionary breakthroughs in technology, and deep transformation and upgrading of industries. (2) The development of NQPFs exhibits a slow upward trend; however, the outbreak of the pandemic and Sino-US trade frictions have caused significant disruptions to the development of new-type productive forces. (3) The level of intelligent manufacturing continues to improve; however, from 2020 to 2023, due to the impact of the COVID-19 pandemic and Sino-US trade conflicts, the level of intelligent benefits has slightly declined. (4) NQPFs exert a powerful driving force on the intelligent transformation of manufacturing, exerting a significant positive impact on intelligent technology, intelligent applications, and intelligent efficiency levels.

1. Introduction

New quality productive forces are characterized by innovation playing a leading role, breaking away from traditional economic growth models and paths of productive force development, and featuring high technology, high efficiency, and high quality, aligning with the advanced state of productive forces under the new development philosophy. Accelerating the development of new quality productive forces is an inevitable requirement for achieving Chinese-style modernization and high-quality economic development. China has listed “vigorously advancing the construction of a modern industrial system and accelerating the development of new quality productive forces” as its top priority, with new quality productive forces becoming a key policy term for understanding future efforts to promote high-quality development.

China remains committed to focusing its economic development efforts on the real economy, striving to lay a solid foundation for a modernized economic system. Promoting the intelligent transformation of manufacturing is an inevitable requirement of Chinese-style modernization. Manufacturing is the backbone of the real economy and the lifeblood of the national economy. As global manufacturing accelerates its transition toward intelligence, China is undergoing a critical leap from a “manufacturing powerhouse” to a “manufacturing superpower.” Facing challenges such as inadequate manufacturing infrastructure and shortages of advanced intelligent equipment, new quality productive forces—with digital technology as a key element—are a crucial driver for the intelligent transformation of manufacturing.

Based on a systematic review of existing literature, this paper focuses on national strategy and global industrial transformation, and systematically innovates in three aspects—research perspective, research philosophy, and research content—around the core proposition of “how new quality productive forces empower the intelligent transformation of manufacturing.” Specifically, first, innovation in research perspective. Existing research has primarily focused on single dimensions, either starting from the intelligent transformation of manufacturing itself or from the measurement and evolution of new quality productive forces. This paper breaks away from the traditional “manufacturing-centric” approach, placing “new quality productive forces” and “manufacturing industry’s intelligent transformation” on the same analytical dimension. It integrates macro perspectives with micro mechanisms, starting from the logical chain of “new quality productive forces—three major mechanisms—intelligent transformation,” systematically revealing how new quality productive forces drive the manufacturing industry’s intelligent leap through intrinsic mechanisms. Second, innovation in research philosophy. This paper breaks away from the traditional “single-point breakthrough” mindset, concretizing the value implications of new quality productive forces into a systematic theoretical framework driven by the synergistic interaction of three mechanisms: the “technological revolutionary breakthrough mechanism,” the “innovative allocation of production factors mechanism,” and the “deep industrial transformation and upgrading mechanism.” This transforms the “value implications” of new quality productive forces from abstract concepts into measurable, verifiable, and replicable mechanism pathways, providing policymakers with a “mechanism-policy-effect” decision-making basis. Third, research content innovation. A “3322” evaluation indicator system for new quality productive forces and a three-dimensional evaluation indicator system for the intelligent transformation of manufacturing are constructed to achieve a systematic and multi-dimensional characterization of new quality productive forces and intelligent transformation.

2. Literature Review

In the wave of the new round of industrial revolution, the intelligent transformation of the manufacturing sector has become a critical component of high-quality economic development. Through technological revolutions, industrial upgrading, and the innovative allocation of production factors, NQPFs drive substantial improvements in production efficiency and the optimization and upgrading of economic structures, thereby providing fresh momentum for the intelligent transformation of manufacturing.

2.1. Research on New Quality Productive Forces

Research on new quality productive forces (NQPFs) has primarily focused on two key aspects: conceptual definition and measurement standards.

From a theoretical perspective, productivity represents a fundamental concept in historical materialism that explains how modes of production determine social development across different historical periods [1]. Marx established that productive forces constitute an objective reality that is not subject to individual choice [2]. The essential distinction between NQPFs and traditional productive forces lies in their inherent reliance on scientific and technological innovation, as well as their pursuit of high-quality development [3].

In practical terms, NQPFs embody the concrete application of China’s productivity theory and serve as a crucial pillar for navigating complex global challenges while advancing domestic modernization [4,5,6]. With innovation at its core, NQPFs drive technological breakthroughs and facilitate profound industrial upgrading [7,8]. By integrating cutting-edge technologies, it overcomes the limitations of traditional growth models, achieving both operational efficiency and qualitative improvements in economic development [9,10].

Conceptually, research grounded in political economy and historical materialism [11,12] interprets the “new” in NQPFs as representing transformative innovation that transcends conventional models. This innovation encompasses both incremental advancements and radical breakthroughs, particularly in fields such as artificial intelligence, which are reshaping modern productivity [13,14,15]. Enterprises that successfully convert advanced technologies into productive capabilities are leading this transformation, which manifests through enhanced efficiency, elevated technical standards, improved ecological sustainability, and more harmonious human-nature relations [16].

The qualitative dimension of NQPFs particularly emphasizes the importance of both foundational technological innovations and disruptive technological advancements. Scholars have noted that NQPFs align perfectly with the requirements of high-quality development, demonstrating superior integrative capacity and substantive depth in the digital era [17,18]. From a factor analysis perspective, NQPFs represent the comprehensive enhancement of all production elements—including labor, production materials, and their optimal combinations—forming a development pathway rooted in human capital advancement, driven by technological innovation, and achieved through efficient resource allocation [9,19].

In assessing the development level of new quality productive forces, scholars have developed various indicator systems and employed different methods to measure the development level of new quality productive forces. Regarding the construction of indicator systems, some researchers have constructed indicator systems based on the fundamental elements of productivity (labor, tools, and objects) to evaluate NQPF development across China’s provinces [6,20,21,22]. For instance, certain scholars have identified innovative productivity, factor productivity, and digital productivity as primary indicators in their assessment frameworks [23]. Others have developed comprehensive evaluation systems by selecting representative indicators at micro, meso, and macro levels [24].

There are two main methods for measuring the level of development of new productive forces. The first approach involves constructing comprehensive indicators at either enterprise or regional levels, utilizing multi-indicator techniques like the Entropy Weight method for quantitative assessment [25,26]. This method enables multidimensional performance evaluation through weighted analysis. The second methodology employs computational tools such as Python3.8 to extract and analyze “new quality productivity” related terminology from official documents and corporate reports, using keyword frequency analysis to evaluate development progress [27]. This text mining approach reveals policy and implementation trends through semantic analysis. While the former emphasizes quantitative precision, the latter provides qualitative insights, together offering complementary analytical perspectives for productivity research.

2.2. Research on Intelligent Manufacturing

The concept of smart manufacturing originated in the 1980s. Wright and Bourne (1988) [28] defined smart manufacturing as “the process by which intelligent robots independently complete production without human intervention by integrating knowledge engineering, manufacturing software systems, and robot vision.” Subsequently, Thoben et al. (2017) [29] proposed that smart manufacturing is a system that applies information technology to implement corresponding processes efficiently and promptly at the shop floor level and above. Davis et al. (2012) [30] argued that smart manufacturing aims to optimize product production and transactions by leveraging advanced information and manufacturing technologies to enhance the flexibility of manufacturing processes, thereby addressing the dynamic global market.

Chinese scholars began researching intelligent manufacturing in the 1990s. Cheng pointed out that due to the weak foundation of China’s manufacturing industry, the initial stage of intelligent manufacturing in China spanned a relatively long period of time. However, through scale expansion, technology introduction, and imitative learning, technological innovation can be achieved, leading to the development of preliminary intelligent manufacturing [31]. Wang and Tao argue that the essence of manufacturing intelligence is not limited to equipment investment and technological application but should also emphasize the economic and social benefits it brings. High-quality development of manufacturing has become a key factor driving the comprehensive progress of the national economy and society [32]. Sun et al. (2024) [33] pointed out that high-quality development has far-reaching significance for the comprehensive progress of the national economy and society. Yu (2020) [34] emphasized that manufacturing is the foundational industrial support and important guarantee for building a socialist modernized powerhouse. It is not only a core component of China’s high-quality economic development but also marks a new stage in China’s industrial development. Jia (2016) [35] proposed that industrial intelligence centered on smart manufacturing represents a new type and advanced stage of industrialization.

The intelligent transformation of manufacturing is accelerating. Yang et al. (2022) [36] noted that manufacturing intelligence is the process of promoting the intelligent transformation of manufacturing or enterprises through the use of technologies such as artificial intelligence and information and communication technology. Lee et al. (2022) [37] emphasized that intelligent transformation based on industrial robots is crucial to the intelligent revolution. Syam and Sharma (2018) [38] argued that artificial intelligence and robotics have become the key drivers of the Fourth Industrial Revolution, leading manufacturing and society toward intelligent transformation. These studies indicate that the application of emerging technologies provides strong technical support for the intelligent transformation of manufacturing, driving its upgrading and transformation.

Intelligent transformation has had a multifaceted positive impact on the development of manufacturing. Purdy and Daugherty (2016) [39] point out that intelligent transformation can assist human work through technologies such as human-machine collaboration and deep learning, achieving intelligent production and management, thereby enhancing labor productivity. Liu and Chen (2021) [40] found that intelligent transformation has a significant impact on the upgrading, rationalization, and transformation of industrial structures. Tang and Chi (2021) [41] further found that intelligent transformation can promote high-quality development in manufacturing by optimizing the labor force structure and improving production efficiency. Huang et al. (2024) [42] proposed that intelligent manufacturing can enhance energy productivity in manufacturing, and its impact becomes more pronounced as human capital levels, economic development levels, and information technology levels improve. Additionally, research by Graetz and Michaels (2018) [43] and Koch et al. (2019) [44] also indicates that intelligentization can increase labor productivity. Smart manufacturing is a new production method characterized by self-perception, self-learning, self-decision-making, self-execution, and adaptability, and has become a key strategic choice for global manufacturing enterprises to address technological changes and achieve high-quality development [45,46]. Major economies have implemented comprehensive manufacturing development plans, such as the United States’ “Advanced Manufacturing Leadership Strategy,” Germany’s “Industrial Strategy 2030,” and Japan’s “Society 5.0.” These strategic frameworks leverage smart manufacturing as the cornerstone for establishing competitive advantages in global industrial transformation. China is also actively advancing the intelligent transformation of its manufacturing sector. In 2015, the Chinese government unveiled the “Made in China 2025” strategy, positioning smart manufacturing as the primary driver of the nation’s efforts to become a manufacturing powerhouse. From 2015 to 2018, the Chinese government implemented multiple batches of national-level smart manufacturing demonstration projects (IMDPs), with pilot enterprises included in the IMDPs having begun to implement smart transformation plans [47].

The intelligent transformation of manufacturing cannot be achieved without the widespread application of artificial intelligence (AI) technology. Scholars have conducted in-depth studies on the application and impact of AI in manufacturing from various perspectives. On one hand, as AI technology continues to evolve, companies may use automated robots to replace some labor, thereby reducing the demand for labor and wage levels [48,49], leading to a decline in the share of labor income [50], a phenomenon known as the substitution effect. On the other hand, the widespread application of intelligent technologies can significantly improve production efficiency, promote capital accumulation and output growth, and thereby drive increases in employment levels and wages [51]. Additionally, the intelligent transformation process can also create a large number of new job opportunities, reflecting the creation effect [52,53]. Therefore, the application of intelligent technology does not necessarily lead to an unlimited expansion of factor income disparities but may instead stabilize or even improve them [49,54]. This balancing effect indicates that the development of intelligent manufacturing is not only an inevitable choice driven by technological progress but also an important force in promoting economic structural adjustment and social sustainable development.

2.3. Research on the Impact of NQPFs on the Transformation of Manufacturing

New quality productive forces is a brand-new concept proposed by China. Currently, foreign scholars primarily focus on the impact of digital transformation [55,56], technological innovation [57,58], and industrial agglomeration [59] on manufacturing transformation.

Domestic scholars’ research on the impact of new quality productive forces on the transformation of the manufacturing industry has mainly focused on theoretical foundations and impact pathways. Manufacturing intelligence is an important manifestation of the new quality of manufacturing, the formation of a new “technology-economic paradigm” in manufacturing. Building on Kuhn’s paradigm theory [60], Perez proposed the “technology-economic paradigm” theory [61], which posits that new technological revolutions drive revolutionary changes in the entire economic system, leading to major transformations from technological structure to industrial structure, and ultimately to social institutional structure, thereby forming a new “technology-economic paradigm. ”Xu and Zhang (2024) [62] proposed the “factor-structure-efficiency” transmission model, which reveals the specific pathways through which new productive forces exert their influence; by enhancing workers’ skills, promoting the digitization of labor tools, and diversifying labor objects, this process facilitates the iteration of production methods and the optimization of resource allocation, thereby advancing the upgrading of the manufacturing sector’s structure and the leap in the technological complexity of exports [63], thereby forming a closed-loop process of “technological breakthrough → value chain upgrading → enhanced supply chain resilience” [64]. From the perspective of total factor productivity (TFP), new quality productive forces directly enhance manufacturing TFP by optimizing the scale and network effects of data elements [65]. The role of new quality productive forces exhibits significant spatial heterogeneity. Zhao and Li (2024) [66] point out that its enabling effect on industrial chain modernization decreases in a “east-central-west” gradient, while Cai (2025) [67] finds that its marginal effects are more pronounced in regions with advantageous business environments. Li et al. (2020) [68] further reveal the regional differentiation characteristics of TFP growth, arguing that western regions achieve faster growth due to their late-mover advantage, while eastern regions rely on technological innovation to drive high-level upgrades.

In summary, existing research provides important references and a solid foundation for this study. However, research on how new quality productive forces can empower the intelligent transformation of manufacturing is still insufficient. The impact mechanism of new quality productive forces on the intelligent transformation of manufacturing and the underlying logic requires more in-depth research. The measurement of the level of intelligent transformation in manufacturing and the development level of new quality productive forces also requires more scientific and detailed research. Based on this, this paper attempts an integrated innovation in methodology at the level of perspective–mechanism–measurement. Taking “new quality productive forces empowering the intelligent transformation of manufacturing” as a new perspective for national-level analysis, this study explores the triple synergistic mechanism and constructs a systematic analytical framework with causal chain characteristics. Additionally, using national-level data from 2012 to 2023, this study measures the levels of new quality productive forces and the intelligent transformation of manufacturing, thereby completing a closed-loop research paradigm from conceptual explanation to mechanism analysis to empirical operation.

3. Mechanism Analysis: How NQPFs Drive the Intelligent Transformation of Manufacturing

Building upon the clarified theoretical foundation, this paper categorizes the mechanisms through which NQPFs drive the intelligent transformation of manufacturing into three key types: innovative allocation mechanism of production factors, revolutionary breakthrough mechanism in technology, and deep transformation and upgrading mechanism of industry. Together, these mechanisms jointly support the intelligent and qualitative transformation of traditional industries.

3.1. Innovative Allocation Mechanism of Production Factors

NQPFs are reshaping the structure of manufacturing production factors and driving industrial upgrading and high-quality economic development. The innovative allocation of these factors has become a core driving force behind the intelligent transformation of manufacturing. At the heart of this mechanism is data, which acts as a core production element [69]. Through its renewability, inclusiveness, and low consumption, data facilitates a shift from a traditional resource-driven model to a modern, data-driven model in manufacturing. Data’s renewability enables continuous optimization through feedback from production and operations; its inclusiveness promotes collaborative data use among multiple stakeholders, enhancing coordination along the value chain, and its low consumption helps manufacturing break away from resource-dependent development. The deep integration of data elements with computing power and algorithms furthers the formation of new productive configurations, optimizes production organization and market-matching mechanisms, and gives rise to knowledge-intensive industrial clusters and innovative models such as the sharing economy. China’s rapidly growing industrial big data and consumer data provide a solid foundation for the intelligent transformation of manufacturing, becoming a key pillar in this transition.

At the level of laborers, NQPFs demand more advanced skills. The intelligent transformation of manufacturing requires workers to possess a blend of operational skills, data analysis capabilities, and equipment maintenance knowledge [70]. By restructuring knowledge systems and integrating vocational education with industry, high-skilled talent capable of adapting to intelligent production can be cultivated. This not only enhances productivity but also enables enterprises to participate in the high-end segments of global value chains, thus improving industrial resilience. Meanwhile, the objects of labor are also undergoing profound changes. Data, digital twin models, and customized service demands have emerged as new objects of labor. Moreover, the tools of labor are being rapidly upgraded. The development of NQPFs is accelerating the shift from traditional mechanical equipment to intelligent systems. Industrial internet technologies and AI-enabled control systems are endowing production equipment with capabilities such as self-perception, autonomous decision-making, and self-execution. These innovations enable flexible production lines to quickly adapt to product variation and meet personalized customization needs.

3.2. Mechanism of Revolutionary Technological Breakthroughs

NQPFs are reshaping the developmental landscape of manufacturing through mechanisms of revolutionary technological breakthroughs, thereby accelerating the sector’s transition toward intelligentization [71,72]. This transformation displays strong systemic characteristics, with technological innovation at its core. The application of frontier technologies such as digital twins and the industrial internet has enabled the intelligent transformation of traditional production processes and the leap from isolated breakthroughs to full-chain upgrades. This qualitative shift has significantly enhanced the level of digitization in the real economy and promoted the high-quality development of manufacturing [73]. In this process, the bidirectional empowerment mechanism between science and industry plays a critical role, facilitating a fundamental shift from traditional production models to intelligent production systems.

At the same time, the emerging demands in the course of manufacturing’s intelligent transformation continue to stimulate technological innovation, forming a virtuous cycle of demand-driven innovation and technology supply. This interactive feedback further reinforces the momentum of revolutionary technological breakthroughs. With regard to the restructuring of production systems, NQPFs drive collaborative transformation across three dimensions—process innovation, workflow reengineering, and business model innovation—thereby constructing the foundational support system for intelligent manufacturing. For example, digital modeling and simulation technologies enable continuous optimization of production processes. The Internet of Things (IoT) facilitates intelligent monitoring and control of production workflows. Cloud computing gives rise to innovative models such as service-oriented manufacturing. These transformations not only improve production efficiency and enhance competitiveness in the manufacturing sector but also redefine value creation mechanisms and reshape industrial ecosystems, injecting new vitality into high-quality economic development.

Moreover, through the shift in the “technology–economy” paradigm, NQPFs have built a comprehensive technical system. This system is supported by foundational general-purpose technologies, carried by intelligent equipment technologies, centered around industrial software technologies, and connected through Industrial Internet technologies. It propels revolutionary changes on the supply side of manufacturing. Against the backdrop of profound adjustments in the global economic landscape, the deep integration of digitalization and network-based technologies has significantly improved the efficiency of information acquisition, processing, and sharing. This has enabled transformative advancements in manufacturing equipment, service platforms, and management systems. NQPFs revitalize innovation in manufacturing through a dual engine of “data × algorithms”, restructuring the sector’s innovation ecosystem. Specifically, at the product development stage, it supports virtual simulation and collaborative design. In the production process, it facilitates precise control and intelligent scheduling. For management and decision-making, it enables globally optimized solutions. In particular, large-scale models have become a key tool for processing massive manufacturing datasets and are emerging as new engines for knowledge creation and technological innovation. This paradigm shift in technology is propelling manufacturing from experience-driven to data-driven, and from local optimization to globally intelligent operations.

3.3. Mechanism of Deep Industrial Transformation and Upgrading

Current international scholarship primarily examines the impacts of digital transformation [55,56], technological innovation [57,58], and industrial agglomeration [59] on manufacturing transformation, with limited attention to the role of new quality productive forces (NQPFs).

Domestic Chinese research on NQPF’s influence on manufacturing transformation has mainly focused on theoretical foundations and impact pathways, with existing empirical studies predominantly confined to regional-level analyses.

Manufacturing intelligence represents a crucial manifestation of manufacturing’s qualitative transformation under NQPFs, the formation of a new “techno-economic paradigm.” Building on Kuhn’s paradigm theory [26] and Perez’s techno-economic paradigm framework [27], technological revolutions drive comprehensive transformations across economic systems, generating fundamental changes from technological structures to industrial and social institutional configurations. Xu Zheng and Zhang Jiaoyu (2024) [28] proposed a “factors-structure-efficiency” transmission model elucidating NQPF’s operational pathways: through enhancing labor skills, digitalizing production materials, and diversifying labor objects, it achieves production mode iteration and resource allocation optimization. This process promotes both manufacturing structure advancement and export technology sophistication [29], forming a “technological breakthrough → value chain ascension → industrial chain resilience enhancement” virtuous cycle [30].

From a total factor productivity (TFP) perspective, NQPFs directly elevate manufacturing TFP by optimizing data elements’ scale and network effects [31]. Its impacts exhibit significant spatial heterogeneity: Zhao Xing and Li Xiangqian (2024) [32] identified an “east–central–west” gradient in its empowerment of industrial chain modernization, while Cai Yanze (2025) [33] found more pronounced marginal effects in regions with superior business environments. Li Lianshui et al. [34] further revealed regional TFP growth disparities, with western regions benefiting from late-development advantages and eastern regions driven by technological innovation for high-end upgrading.

In summary, while existing research provides valuable foundations, it lacks investigation into the fundamental mechanisms underlying NQPF’s empowerment of manufacturing intelligent transformation (Figure 1). A deeper analysis is required to understand these underlying logics. Additionally, current studies remain constrained by regional data, lacking comprehensive national-level longitudinal examinations.

4. Research Design

4.1. Sample Selection and Data Sources

Since the 18th National Congress of the Communist Party of China, the country has achieved significant progress across economic, political, social, and other domains. Based on the availability and consistency of relevant data, this study selects the period from 2012 to 2023 as the analytical timeframe, covering national-level indicators across various dimensions, including economy, politics, culture, society, ecology, population, and military. The objective is to evaluate both the development level of NQPFs and the level of intelligent transformation in the manufacturing industry. The data used for constructing the evaluation indicators are primarily sourced from official statistical yearbooks, including the China Statistical Yearbook, China Science and Technology Statistical Yearbook, and China Torch Statistical Yearbook, as well as statistical reports such as the China Shared Economy Development Report and publicly available datasets from the National Bureau of Statistics of China. Missing data were supplemented using interpolation techniques, and some derived indicators were calculated based on these primary data sources.

In summary, existing research provides important references and a solid foundation for this study. However, there is a lack of research on the underlying logic behind the impact mechanism of new quality productive forces on the intelligent transformation of manufacturing. We need more in-depth analysis to understand the underlying logic of new quality productive forces empowering the intelligent transformation of manufacturing. In addition, current research often focuses on local-level data and lacks tracking and discussion of national-level data.

4.2. Model Specification and Variable Selection

To empirically examine the effect of NQPFs on the intelligent transformation of the manufacturing sector, the following econometric model is specified:

$${\mathrm{Y}}_{\mathrm{i},\mathrm{t}}={\mathsf{\alpha }}_0+{\mathsf{\beta }}_1{·\mathrm{N}\mathrm{P}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\beta }}_2{·\mathrm{Z}}_{\mathrm{i},\mathrm{t}}+{\mathsf{\lambda }}_{\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$$

Among these, ${\mathrm{Y}}_{\mathrm{i},\mathrm{t}}$ represents the comprehensive intelligentization level of the manufacturing sector in year ii, encompassing the development level of intelligent technology, the application level of intelligent technology, and the benefit level of intelligent technology; ${\mathrm{N}\mathrm{P}\mathrm{Q}}_{\mathrm{i},\mathrm{t}}$ represents the principal component score of new quality productive forces in year ii; ${\mathrm{Z}}_{\mathrm{i},\mathrm{t}}$ is the control variable vector, which includes influencing factors such as government expenditure (Govern), cost (Cost), economic level (Eco), education investment (Edu), fiscal policy (Fiscal), industrial structure (Ind), and labor market (Labor); ${\mathsf{\lambda }}_{\mathrm{t}}$ is the time fixed effect, used to control for the impact of macroeconomic fluctuations and policy adjustments at different time points on the results; ${\mathsf{\epsilon }}_{\mathrm{i},\mathrm{t}}$ is the random error term, representing the unexplained portion of the model.

To reduce result bias caused by omitted variables, this paper includes the following control variables in its empirical analysis: degree of government intervention, social consumption level, level of economic development, level of economic development, level of education expenditure, industry index, and labor index.

4.3. Research Methodology

This study employs the Entropy Weight–TOPSIS method, a hybrid evaluation model. First, the entropy method is used to determine the weights of multiple indicators, minimizing subjectivity in the weighting process. Then, TOPSIS is applied to rank alternatives based on proximity to an ideal solution. This method offers several advantages: objectivity, intuitive interpretability, low information loss, and computational flexibility. However, the Entropy Weight–TOPSIS method also has some inherent limitations, which are particularly evident when dealing with extreme values. Since the entropy method relies heavily on the dispersion of indicators when calculating weights, the existence of extreme values may have a significant impact on the calculation of entropy values, leading to unreasonable weight distribution.

The modeling procedure is as follows:

Let there be mth evaluation indicators across n evaluation years. Let $X_{ij}$ represent the original data of the jth indicator in the ith year (i = 1, 2, …, n; j = 1, 2, …, m).

(1) Data Standardization. Since the indicators differ in units and scales, they are standardized to ensure comparability.

For benefit-type (positive) indicators,

$$X_{ij}={\displaystyle \frac{X_{ij}-X_{min}}{X_{max}-X_{min}}}+D$$

To avoid negative values during the calculation of entropy, which would render the logarithmic function undefined, a minimal translation adjustment is necessary. In this study, a constant D = 0.001 is uniformly added to the standardized data (i.e., a rightward shift of 0.001).

After this standardization process, the standardized matrix can be obtained.

$$Y=\left|\begin{array}{ccc}{y}_{11}& y_{12}\dots & y_{1n}\\ \cdots & \dots \dots & \dots \\ y_{m1}& y_{m2}\dots & y_{mn}\end{array}\right|$$

(2) Calculate the proportion of each indicator $P_{ij}$

$$p_{ij}={\displaystyle \frac{X_{ij}}{\sum X_{ij}}}$$

and the characteristic proportion matrix is obtained:

$$R=\left|\begin{array}{ccc}{r}_{11}& r_{12}\dots & r_{1n}\\ \cdots & \dots \dots & \dots \\ r_{m1}& r_{m2}\dots & r_{mn}\end{array}\right|$$

(3) Calculate the information entropy for each indicator:

$$e_j={\displaystyle \frac{1}{{ln}_m}}\sum _{i=1}^m{p}_{ij}{lnr}_{ij}$$

(4) Determine the weight coefficients W:

$$w_j={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^n(1-e_j)}}$$

(5) Construct the normalized decision matrix (TOPSIS)

$$G=\left|\begin{array}{ccc}{g}_{11}& g_{12}\dots & g_{1n}\\ \cdots & \dots \dots & \dots \\ g_{m1}& g_{m2}\dots & g_{mn}\end{array}\right|$$

(6) Build the weighted normalized decision matrix Z

$$z_{ij}=w_j\ast g_{ij}$$

$$Z=\left|\begin{array}{ccc}{z}_{11}& z_{12}\dots & z_{1n}\\ \cdots & \dots \dots & \dots \\ z_{m1}& z_{m2}\dots & z_{mn}\end{array}\right|$$

(7) Determine the positive ideal solution:

Positive ideal solution $Z_j^{+}$:

$$Z_j^{+}=\left[z_1^{+},z_2^{+},\cdots ,z_n^{+}\right](j=\mathrm{1,2},\cdots ,n)$$

(8) Calculate the separation distances of each evaluation object from the two ideal solutions:

$$d_j^{+}=\sqrt{{\sum }_{i=1}^n{(z_{ij}-z_j^{+})}^2}$$

(9) Calculate the closeness coefficient of each evaluation object to the positive ideal solution:

$${ S}_i={\displaystyle \frac{d_j^{-}}{d_j^{+}+d_j^{-}}}(1\le i\le m)$$

The closer closeness is to 1, the closer the evaluation object is to the ideal level.

Using this Entropy–TOPSIS method, this study constructs an intelligent transformation of manufacturing. The method is then used to compute the weights of the indicators.

4.4. Construction of the Evaluation Index System for the Intelligent Transformation of Manufacturing and Indicator Weights

In this study, the dependent variables include three dimensions: intelligent technology, intelligent application, and intelligent benefits. The final index system is shown in

Table 1. Evaluation index system for the intelligent transformation of manufacturing.

Primary Dimension	Secondary Indicator	Description	Unit	Attribute
Intelligent Technology	Industrial robot deployment	Output of industrial robots	10,000 sets	Positive
	Fixed asset investment	Per capita fixed asset investment in IT and software sectors	CNY/person	Positive
	Optical cable coverage	Cable length per area unit	1/m2	Positive
	Industrial Internet infrastructure	Number of computers per 100 people	Units	Positive
Intelligent Application	System integration	Main business income of IT services	CNY 100 million	Positive
	New product projects	Number of new manufacturing product development projects	Projects	Positive
	R&D expenditure	Development expenditure on new manufacturing products	CNY 10,000	Positive
	Patent applications	Number of invention patent filings in manufacturing	Cases	Positive
Intelligent Benefits	Operating profit	Profit of high-tech manufacturing enterprises	CNY 100 million	Positive
	Export performance	Export value of high-tech products	CNY 100 million	Positive

. Intelligentization in manufacturing is a multilayered and integrated process, centered on the deep integration of emerging information technologies with traditional manufacturing. This transformation spans the entire value chain—from R&D and design to production and operations—and enables manufacturing to advance toward digitalization, networking, and intelligence. Following the principles of systematic design, operational feasibility, and data availability, and drawing on the methodological framework of [74], this paper develops an evaluation system with three core dimensions: intelligent technology, intelligent application, and intelligent benefits. Intelligent technology serves as the foundation of smart manufacturing, providing the essential support for intelligent applications and intelligent benefits. It constitutes the physical basis and prerequisite for the intelligent transformation of the manufacturing industry. In this study, intelligent technology is measured using four indicators: the application volume of industrial robots, fixed asset investment, optical cable coverage, and industrial internet infrastructure. Industrial robots are the physical execution units of the intelligent technology system and determine the effectiveness of implementing intelligent manufacturing. Fixed asset investment is the driving force behind technological advancement, propelling innovation in intelligent technologies. Optical cables function as the medium for data transmission, establishing the channels for information flow. The industrial internet is a critical infrastructure for intelligent manufacturing, offering the foundational pathways for its development.

Intelligent application is a key link in enhancing smart manufacturing capabilities, with the core of “intelligence” lying in the integration and innovation of intelligent technologies. The indicators of intelligent application reflect the level of technological development and innovation in the process of manufacturing intelligence. In this study, intelligent application is measured through two dimensions: system integration, represented by the main business revenue of the software and information technology services industry, and product innovation, represented by the number of new product development projects in manufacturing, R&D expenditure, and the number of invention patent applications. The software and information technology sector is a critical force driving intelligent transformation; it is a foundational, strategic, and pioneering industry crucial to the overall development of society. New products in the manufacturing industry serve as a key embodiment of intelligent application capabilities and are central to meeting future challenges.

Intelligent benefits assess whether intelligent manufacturing is achieving its intended effects within the market environment and serve as an important criterion for evaluating the success of intelligent transformation. As one of the goals of intelligent transformation, intelligent benefits are a vital component of manufacturing intelligence. In this study, intelligent benefits are measured by the operating profit of high-tech industries and the export value of high-tech products across regions. Operating profit objectively reflects the improvement in operational efficiency brought about by intelligent transformation, while export performance systematically evaluates the impact of intelligent manufacturing on international competitiveness.

With the help of the entropy weight-TOPSIS method, we get the index weights, as shown in

Table 2. Weights of indicators in the evaluation system.

Primary Dimension	Secondary Indicator	Description	Weight (%)
Intelligent Technology	Industrial robot deployment	Output of industrial robots	14.30%
	Fixed asset investment	IT/software fixed investment per capita	5.70%
	Optical cable coverage	Cable length per area	8.50%
	Industrial Internet infrastructure	Computers per 100 persons	8.76%
Intelligent Application	System integration	IT services revenue	8.59%
	New product development projects	Manufacturing innovation activity	15.32%
		R&D investment for new products	10.45%
		Invention patent filings	9.95%
Intelligent Benefits	Operating profit	High-tech manufacturing profits	7.68%
	Export performance	Export value of high-tech products (billions of CNY)	10.75%

.

Draw the weight chart as shown in Figure 2 based on the evaluation index weights.

4.5. Construction of the Evaluation Index System for New Quality Productive Forces and Indicator Weights

The core explanatory variable in this study is NQPFs. As an upgrade and innovation over traditional productive forces, NQPFs encompass multiple dimensions, including production factors, key driving forces, and support systems. It is more aligned with the strategic requirements of China’s modernization drive.

Drawing on the research frameworks proposed by scholars such as Lu Jiang [26] and Wang Jue [22], and in reference to the Research Report on New Quality Productive Forces (2024) issued by the China Academy of Information and Communications Technology (CAICT), this study innovatively constructs a theoretical framework for NQPFs referred to as the “3322” model. This model comprises the following: three core production factors: laborers, objects of labor, and means of labor; three key drivers: revolutionary technological breakthroughs, innovative allocation of production factors, and deep industrial transformation and upgrading; two support systems: internal institutional support and external openness; two defining characteristics: optimized structural combination and high-quality development. The theoretical structure is illustrated in Figure 3.

The index system is structured according to four criterion layers: formative process, key drivers, support systems, and defining characteristics, and it systematically captures the multidimensional characteristics of NQPF development. The Formative Process focuses on the modernization and upgrading of the three fundamental elements of productivity—laborers, objects of labor, and means of labor—reflecting the evolving substance of production factors. The key drivers include technological breakthroughs, deep industrial transformation and upgrading, and innovative allocation of production factors, which together reveal the central role of innovation-driven development and industry–technology integration. These drivers collectively propel the qualitative leap in productive capacity. The support systems refer to internal policies and external openness. Coordinated efforts in domestic policy support and institutional innovation form the foundation for NQPF development, while high-level external openness provides institutional guarantees and developmental space. The defining characteristics evaluate the outcomes of productivity transformation through the two dimensions of optimized factor combination and high-quality development. At a critical juncture of economic transformation and industrial upgrading, aligning with the new development philosophy and responding to national strategic needs—especially in light of General Secretary Xi Jinping’s emphasis on the innovation-driven development strategy and scientific and technological self-reliance and strength—is imperative for advancing NQPFs.

Based on this framework, the study constructs the NQPF evaluation index system (see

Table 3. Evaluation index system for NQPFs.

Criterion Layer	Primary Indicator	Secondary Indicator	Tertiary Indicator	Attribute
Formative Process	Laborers	Training Investment	Human capital investment	Positive
			Scientific investment	Positive
			Educational investment	Positive
		Labor Output	Contribution of the computer industry	Positive
			Contribution of R&D and tech services	Positive
			Output per capita	Positive
			Wage per capita	Positive
		Labor Quality	Health quality level	Positive
			Employment perception level	Positive
			Innovation awareness of laborers	Positive
	Objects of Labor	New energy	Share of renewable energy generation	Positive
			Number of UHV transmission lines	Positive
		Data elements	Big data generation	Positive
			Big data processing	Positive
		New quality industries	High-tech industries	Positive
			Future industries	Positive
			Advanced manufacturing	Positive
			Electronic information industries	Positive
	Means of Labor	Infrastructure	Traditional infrastructure	Positive
			Digital infrastructure	Positive
		Production tools	Integrated circuit output (100 million units)	Positive
Key Drivers	Technological Breakthrough	Scientific innovation productivity	Innovation investment	Positive
			Innovation output	Positive
			Innovation and entrepreneurship ecosystem	Positive
		Digital technology	Digital informatization	Positive
			Digital interconnectivity	Positive
			Digital economy development level	Positive
			Industrial digitalization	Positive
			Level of digital application	Positive
	Deep Industrial Transformation	Green transformation	Resource consumption level	Positive
			Ecological governance capacity	Positive
			Environmental protection	Positive
		Informatization	Informatization investment level	Positive
			Informatization capacity	Positive
			Informatization output	Positive
		High-end industries	New quality industrial chains	Positive
			New services	Positive
	Innovative Factor Allocation	Factor demand	Demand for technology	Positive
			Demand for knowledge	Positive
			Demand for land	Positive
		Productive relations restructuring	New quality industry clusters	Positive
			Emerging shared economy models	Positive
			Production efficiency	Positive
			Production quality	Positive
Support Systems	Internal Support System	Fiscal policy	National fiscal allocation for science & technology	Positive
			Education expenditure	Positive
			Science expenditure	Positive
			Government funding for R&D in large-scale enterprises	Positive
			R&D funding for research and development institutions	Positive
			Government funding in university R&D	Positive
		Sci-tech policy	Number of technology incubators	Positive
			Number of incubated enterprises in incubators	Positive
			Number of makerspaces	Positive
			Startups served by makerspaces	Positive
			Number of national tech transfer institutions	Positive
			Enterprises served by national transfer institutions	Positive
			Number of national university science parks	Positive
			Incubated enterprises in science parks	Positive
		Financial policy	Digital inclusive finance index	Positive
			Number of active VC institutions	Positive
			Number of venture capital firms	Positive
			Investment intensity of venture capital	Positive
	External Support System	Trade openness	Trade structure	Positive
			Foreign trade dependence	Positive
		Financial openness	Capital mobility	Positive
			Openness of financial services trade	Positive
		Investment openness	Foreign investment dependence	Positive
		IT openness	Technological openness	Positive
			Information openness	Positive
		Institutional openness	Actual tariff rate	Positive
			External business environment	Positive
Defining Characteristics	Optimized Structural Combination	Resource optimization	Labor productivity	Positive
			Capital productivity	Positive
			Land productivity	Positive
			Energy productivity	Positive
		Organizational efficiency	Total factor productivity	Positive
			Industrial structure optimization	Positive
		Market optimization	Share of retail sales in total industrial & agricultural output	Positive
			Number of large-scale wholesale/retail enterprises	Positive
	High-Quality Development	Innovation development	Investment efficiency	Positive
			Activity in technology transactions	Positive
			China innovation index	Positive
			Number of new industrial projects	Positive
			Total fixed asset investment in new industries	Positive
		Coordinated development	Urban–rural structure	Positive
			Government debt burden	Positive
		Green development	Safe disposal rate of household waste	Positive
			Energy transformation efficiency	Positive
			Green invention achievements	Positive
			Energy consumption growth rate relative to GDP	Positive
		Open development	Number of FDI contract projects	Positive
			Total amount of FDI	Positive
			Number of foreign-invested enterprises	Positive
		Shared development	Income distribution	Positive
			Social security	Positive
			Public welfare	Positive

) and determines the indicator weights accordingly (see

Table 4. Principal component analysis eigenvalue.

Principal Component	Eigenvalue	Variance Proportion	Cumulative Variance Proportion
Comp1	114.212	0.6964	0.6964
Comp2	21.2109	0.1293	0.8257
Comp3	8.90895	0.0543	0.8801
Comp4	4.88405	0.0298	0.9099
Comp5	3.72296	0.0227	0.9326
Comp6	2.8978	0.0177	0.9502
Comp7	2.5696	0.0157	0.9659
Comp8	1.78239	0.0109	0.9768
Comp9	1.54395	0.0094	0.9862
Comp10	1.2107	0.0074	0.9936
Comp11	1.05696	0.0064	1

).

This paper views new quality productive forces as a key driver of the intelligent transformation of manufacturing. Unlike traditional weighted aggregation methods, this paper employs principal component analysis (PCA) to reduce the dimensionality of a multi-dimensional indicator system, thereby extracting statistical dimensions (principal components) that represent the essence of new quality productive forces.

Based on the aforementioned “3322” theoretical framework, the original indicator system covers four dimensions: formative process, key drivers, support systems, and defining characteristics. It includes 10 primary indicators and several secondary/tertiary indicators, comprehensively reflecting the constituent elements of new quality productive forces. After standardization (Z-score standardization), all indicators were analyzed using PCA in Stata 17. To conduct an in-depth analysis of the key factors influencing new quality productive forces, this study employed principal component analysis (PCA) to perform dimensionality reduction on the multidimensional data. PCA is a commonly used dimensionality reduction technique that transforms multiple highly correlated variables into a few comprehensive principal components to better extract the main features of the data and eliminate noise. During this process, all variables were standardized to ensure they had the same units and standard deviations.

Through principal component analysis, the eigenvalues and variance explained ratios of multiple principal components were calculated. The following are the main statistical results of the top 10 principal components (see Table 4).

Draw the weight chart as shown in Figure 4 based on the evaluation index weights.

5. Results and Analysis

5.1. National Trends in the Development of NQPF

This study measures the national development levels of NQPFs. The results are presented in

Table 5. Measurement results of national NQPFs (2012–2023).

Year	NQPF
2012	0.3483
2013	0.4038
2014	0.4077
2015	0.4762
2016	0.5529
2017	0.5701
2018	0.5378
2019	0.5214
2020	0.3698
2021	0.6348
2022	0.319
2023	0.592

, with the overall trend illustrated in Figure 5.

From 2012 to 2023, the development of NQPFs showed a trend of slow growth at first, followed by high volatility and periodic fluctuations. This progression may be attributed to several key national initiatives:

In 2012, the 18th National Congress of the Communist Party of China emphasized the importance of pursuing indigenous innovation and implementing an innovation-driven development strategy. In July 2015, the State Council issued the Guiding Opinions on Actively Promoting the “Internet Plus” Initiative, aiming to integrate internet-based innovation with various sectors of the economy and society to drive technological advancement, efficiency gains, and organizational transformation—ultimately enhancing innovation and productivity in the real economy. In August 2015, the CPC Central Committee and the State Council released the Guidelines on Deepening Reform of State-Owned Enterprises, promoting mixed-ownership reform and operational efficiency. In December 2015, the Central Economic Work Conference proposed advancing supply-side structural reforms, with a core emphasis on liberating productive forces through deeper reform and improving both the quality and quantity of economic growth. As a result, Chinese enterprises began accelerating their transition toward high-end, intelligent, and green production, leading to marked improvements in productivity.

Additionally, the rapid development of the digital economy provided strong technological support and application scenarios for the rise of NQPFs. At the 2016 G20 Hangzhou Summit, digital economy cooperation was formally proposed. In 2017, the term “digital economy” was included in the report to the 19th National Congress of the CPC. Between 2016 and 2022, China’s digital economy expanded from CNY 2.25823 trillion to CNY 5.39 trillion, catalyzing new industrial forms and business models while accelerating the digital transformation of traditional sectors. This significantly boosted productivity and innovation, becoming a key driver of NQPF’s growth.

In 2016, the State Council issued the “13th Five-Year Plan” for the development of national strategic emerging industries. Strategic emerging industries represent the direction of a new round of technological revolution and industrial transformation. They are key areas for fostering new drivers of growth and gaining new competitive advantages in the future. Under the dual influence of policy guidance and market demand, these industries have gradually become an important component of new productive forces. They not only promote the optimization and upgrading of the industrial structure but also drive collaborative industrial development, forming new economic growth points and industrial clusters that strongly support the development of new productive forces. In the same year, the “13th Five-Year National Science and Technology Innovation Plan” explicitly proposed improving the incentive and evaluation system for the transformation of scientific and technological achievements. It emphasized promoting close integration between the research outcomes of scientific institutions and universities with enterprise needs, thereby accelerating the transformation of scientific and technological achievements into real productive forces. As a result, China’s scientific and technological innovation outcomes have been rapidly translated into practice, and the close integration of research with industrial demand has further accelerated this transformation. The level of new productive forces has achieved sustained and steady improvement, laying a solid foundation for future growth.

In March 2018, the US-China trade war officially began. Based on the results of the “301 investigation,” the Trump administration announced tariffs on approximately USD 60 billion worth of Chinese goods, and China immediately took countermeasures. This trade war increased the costs and uncertainty for Chinese export companies, especially for manufacturing companies that are highly dependent on the US market, leading to a decline in orders and profits for some companies. Meanwhile, the global market volatility and instability triggered by the trade friction made companies more cautious in their investment, R&D, and innovation decisions, resulting in reduced investment in the cultivation and development of new productive forces. As a result, the new productive forces index declined from 0.5701 in 2017 to 0.5378 in 2018.

From late 2019 to early 2020, the COVID-19 pandemic erupted globally. The pandemic disrupted global industrial chains and supply chains, causing many factories to shut down, logistics and transportation to be blocked, and raw material supplies to be insufficient. As a major global manufacturing powerhouse, China faced significant challenges in the development of new quality productive forces, with many companies experiencing production stagnation and order backlogs. At the same time, the pandemic led to a global economic downturn, with a significant contraction in international market demand. China’s export markets were severely impacted, corporate profitability declined, and the ability to invest in and apply new technologies and business models weakened. As a result, the new quality productive forces index further declined to 0.5214 in 2019 and dropped to 0.3698 in 2020.

In 2021, the Chinese government introduced a series of policy measures to support enterprise innovation and development, including tax incentives, fiscal subsidies, and financial support. These policies help reduce enterprise costs and enhance their innovation capabilities and competitiveness. Meanwhile, after more than a year of pandemic control efforts, China achieved significant results in both pandemic prevention and control and socio-economic development. Businesses gradually adapted to production and operational models under the new normal of the pandemic, with resumption of work and production proceeding in an orderly manner, and the economy gradually recovering and growing. This provided a favorable macroeconomic environment for the development of new quality productive forces, leading to an increase in the new quality productive forces index to 0.6348 in 2021.

However, in 2022, the pandemic resurged in some regions of China, leading to strict prevention and control measures in certain areas, which again impacted business production and logistics operations. Some businesses faced challenges such as disrupted raw material supplies, backlogged orders, and tight cash flows, exacerbating operational difficulties. Meanwhile, the United States continued to enforce its long-standing export control and economic sanctions policies, not only strengthening legislative and enforcement efforts but also promoting the multilateralization of these measures, collaborating with allies to impose coordinated sanctions on industries in China and Russia. This has placed Chinese enterprises under greater competitive pressure and trade barriers in international markets, posing external challenges to the development of new quality productive forces. As a result, the new quality productive forces index dropped significantly to 0.3190 in 2022.

In 2023, with the end of the pandemic, the global economy began to gradually recover, and international trade and investment activities gradually resumed. China’s economy also saw an opportunity for recovery, with improved business operating environments and gradually recovering market demand. The economic recovery has spurred investment and innovation across industries, with companies accelerating the adoption and promotion of new technologies and business models, thereby driving the enhancement of new quality productive forces. Meanwhile, the government has continued to increase support for scientific and technological innovation and industrial upgrading, providing policy safeguards for the development of new quality productive forces. As a result, the new quality productive forces index rebounded to 0.5920 in 2023.

5.2. National Trends in Intelligent Transformation in Manufacturing

In recent years, China’s level of intelligent transformation in manufacturing has steadily improved, showing a clear upward trend. This evolution can largely be attributed to three main factors: national policy guidance, changes in the domestic and international economic environment, and the driving force of NQPFs. The results are presented in

Table 6. Measurement results of manufacturing intelligentization levels (2012–2023).

Year	Intelligent Technology	Intelligent Application	Intelligent Benefits
2012	0.0000	0.0000	0.0000
2013	0.0474	0.0563	0.1276
2014	0.1172	0.1024	0.1514
2015	0.1976	0.0990	0.1745
2016	0.3074	0.1694	0.1559
2017	0.4138	0.2593	0.2591
2018	0.4852	0.3509	0.4052
2019	0.5605	0.4592	0.4279
2020	0.6687	0.5796	0.7081
2021	0.8136	0.7564	0.9160
2022	0.9541	0.8875	0.7585
2023	0.9878	1.0000	0.5412

, with the overall trend illustrated in Figure 6.

In 2012, the Ministry of Science and Technology issued the “12th Five-Year Special Plan for the Development of Intelligent Manufacturing Science and Technology,” promoting a forward-looking, innovation-led strategy that laid the theoretical and systematic foundation for intelligent manufacturing. In May 2015, the State Council released Made in China 2025, the first ten-year action plan of the manufacturing power strategy, which aligned with global economic trends and domestic manufacturing needs. Later that year, in September, the Ministry of Industry and Information Technology launched a pilot demonstration initiative for intelligent manufacturing. In December, it published the Guide to the Construction of the Intelligent Manufacturing Standards System. In the 2016 Government Work Report, concepts such as “mass customization” and “flexible production” reappeared, highlighting the government’s determination to promote intelligent manufacturing, large-scale customization, networked collaborative manufacturing, and service-oriented manufacturing. That same year, “Made in China + Internet” was first mentioned in a Government Work Report, stressing the deep integration of Made in China 2025 with the “Internet Plus” strategy. To support this, a CNY 30 billion special fund was established for intelligent manufacturing, and the first national manufacturing innovation center was created. The “industrial foundation enhancement project” was implemented to boost intelligent manufacturing from multiple angles: financial support, innovation platforms, and capability building. In 2017, China launched its first batch of industrial internet platforms. A large volume of high-quality intelligent software emerged and found deep applications across manufacturing industries. The government’s continuous promotion of informatization and industrial integration significantly increased demand for intelligent software solutions. In 2021, eight ministries, including the Ministry of Industry and Information Technology and the National Development and Reform Commission, jointly issued the 14th Five-Year Plan for the Development of Intelligent Manufacturing, providing sustained policy momentum for the transformation and upgrading of industry. A more open, fair, and competitive environment began to take shape—conducive to enterprise innovation and industrial progress—and helped provide long-term momentum and solid guarantees for China’s transition from bigness and mightiness.

At the global level, the industrial landscape has undergone major adjustments. International trade rules are being reshaped, and manufacturing has re-emerged as a strategic focal point in global economic competition. In response, countries have launched re-industrialization strategies centered on revitalizing manufacturing. To secure a stronger position in global value chains, China is accelerating its intelligent transformation, improving productivity, product quality, and innovation capacity to better adapt to global market changes and challenges. Intelligent manufacturing is increasingly becoming a global trend, and both competition and cooperation among enterprises are driving its advancement. Despite the severe impact of the COVID-19 pandemic on the global economy in 2020, China’s smart technology and application levels have surged against the trend, primarily due to the synergistic effects of demand, supply, and policy factors. First, the rigidity of demand has become evident. The pandemic disrupted interregional labor mobility, leading to temporary shutdowns in the traditional production models of labor-intensive enterprises. To mitigate labor shortages and ensure continuous production, enterprises were forced to accelerate the deployment of intelligent technologies such as industrial robots and industrial internet, replacing on-site operations with “contactless production,” thereby creating a sudden surge in demand for intelligent equipment. Second, cost constraints on the supply side intensified. Amidst shrinking terminal market demand and declining revenue, state-owned enterprises and large manufacturing groups still had to fulfill their obligations for rigid wage expenditures, directly increasing unit labor costs. This cost pressure has forced enterprises to accelerate their intelligent transformation efforts, reducing reliance on labor through capital-technology substitution to stabilize production capacity and cash flow. Third, strong policy-driven initiatives. The Ministry of Industry and Information Technology (MIIT) and other departments have mandated the promotion of intelligent applications such as remote maintenance, digital collaboration, and online monitoring to facilitate “cloud-based resumption of work” in manufacturing. The external coerciveness of policies combined with subsidy incentives has further amplified enterprises’ willingness to invest in intelligent technologies. Therefore, while the pandemic suppressed terminal demand and led to a decline in the benefits of intelligent technologies, the combined effects of the rigid demand for “contactless production,” the cost pressure from wage rigidity, and the strong policy-driven impetus have made investments in intelligent technologies and applications a “necessity” for enterprises to maintain operations, thereby achieving counter-cyclical growth, which aligns closely with the trend shown in Figure 6.

In addition, NQPFs have injected strong momentum into the intelligent transformation of the manufacturing industry. By optimizing the scale and network effects of data as a factor of production, NQPFs directly enhance the total factor productivity of manufacturing. They also promote the coordinated development of manufacturing with other industries, leading to the formation of intelligent manufacturing industrial clusters. As a representative of new productive factors, data is a key driver of intelligentization, significantly improving production efficiency through collaborative innovation and knowledge sharing, and driving the intelligent development of manufacturing. Talent is the core element of intelligent transformation in manufacturing. NQPFs provide the industry with highly skilled professionals in intelligent manufacturing, improve workers’ digital and intelligent skills, and promote technological and managerial innovation, injecting fresh vitality into intelligent transformation. Additionally, NQPFs accelerate the process of intelligent transformation in manufacturing through the reallocation of production factors, improvement in total factor productivity, and the implementation of new development concepts.

5.3. Variable Stationarity Test

To avoid the issue of spurious regression caused by the non-stationarity of time series data, it is essential to conduct unit root tests on the variables before model construction to determine the stationarity of the time series. In this study, the ADF (Augmented Dickey–Fuller) unit root test was employed using EViews 12 software to test the variables involved. The test results are presented in

Table 7. ADF unit root test results.

Variable Type	Original Series
	Test Type (C, T, N)	ADF-Value	p-Value	Conclusion
NQPF	(1, 1, 2)	0.682	0.997	Non-stationary
Intelligent Technology	(1, 0, 2)	−1.901	0.588	Non-stationary
Intelligent Application	(1, 1, 0)	−0.862	0.922	Non-stationary
Intelligent Benefits	(1, 1, 2)	−2.791	0.233	Non-stationary
Degree of Government Intervention	(1, 0, 1)	−1.906	0.317	Non-stationary
Level of Social Consumption	(1, 1, 1)	−1.839	0.613	Non-stationary
Level of economic development	(1, 1, 0)	−2.876	0.207	Non-stationary
Level of Education expenditure	(1, 1, 0)	−2.438	0.267	Non-stationary
industry Index	(1, 0, 2)	−1.988	0.408	Non-stationary
labor Index	(1, 1, 2)	0.682	0.997	Non-stationary
Variable Type		First-Difference Series
	Test Type (C, T, N)	ADF-Value	p-Value	Conclusion
NQPF	(1, 1, 2)	−2.38	0.362	Non-stationary
Intelligent Technology	(1, 1, 2)	−3.777	0.074	Non-stationary
Intelligent Application	(1, 1, 0)	−1.308	0.581	Non-stationary
Intelligent Benefits	(1, 2, 2)	−3.443	0.003	Stationary
Degree of Government Intervention	(1, 1, 0)	−3.648	0.079	Stationary
Level of Social Consumption	(1, 1, 1)	−1.936	0.537	Non-stationary
Level of economic development	(1, 1, 0)	−1.973	0.314	Non-stationary
Level of Education expenditure	(0, 0, 1)	−2.343	0.039	Non-stationary
industry Index	(1, 1, 2)	−1.453	0.107	Stationary
labor Index	(1, 1, 2)	−2.38	0.362	Non-stationary
Variable Type	Second-Difference Series
	Test Type (C, T, N)	ADF-Value	p-Value	Conclusion
NQPF	(1, 0, 2)	−3.683	0.036	Stationary
Intelligent Technology	(0, 1, 2)	−2.889	0.01	Stationary
Intelligent Application	(0, 0, 0)	−3.476	0.003	Stationary
Intelligent Benefits	(1, 2, 2)	−2.97	0.009	Stationary
Degree of Government Intervention	(0, 0, 1)	−3.018	0.008	Stationary
Level of Social Consumption	(0, 0, 1)	−2.736	0.009	Stationary
Level of economic development	(1, 1, 0)	−4.342	0.001	Stationary
Level of Education expenditure	(0, 0, 0)	−3.682	0.002	Stationary
industry Index	(0, 1, 2)	−6.57	0.001	Stationary
labor Index	(1, 0, 2)	−3.683	0.036	Stationary

. At the 5% significance level, the time series of the explanatory variables, the dependent variable, and the control variables were all found to be non-stationary. To address this, the variables were first differenced. After first-order differencing, only the variables intelligent benefits, degree of government intervention, and industry index were found to be stationary at the 5% significance level, while the remaining variables remained non-stationary. Therefore, second-order differencing was applied. At the 5% significance level, all variables became stationary after second-order differencing. Based on these results, the construction and estimation of a multiple linear regression model can be appropriately conducted.

5.4. Baseline Regression

Table 8. Baseline regression results.

	(1) Intelligent Technology	(2) Intelligent Application	(3) Intelligent Benefits
NQPF	1.804 **	2.656 **	3.444 **
	(3.82)	(3.56)	(4.19)
Degree of Government Intervention	−1.171 **	−0.284	−0.214
	(−5.60)	(−0.86)	(−0.54)
Level of Social Consumption	1.010 **	0.772	0.731
	(2.58)	(1.89)	(1.12)
Fiscal Investment Level	0.312	−0.333	−0.956 *
	(2.03)	(−1.19)	(−4.48)
industry Index	−0.463 **	−1.013	−1.107
	(−2.12)	(−1.88)	(−1.93)
labor Index	−0.493	−1.065	−0.895
	(−1.09)	(−1.47)	(−1.27)
_cons	0.025	−0.017	−0.004
	(1.60)	(−1.80)	(−0.28)
ind	No	No	No
N	12	12	12
R2	0.875	0.890	0.856
Adj. R2	0.815	0.850	0.803

Note: ** p < 0.05, * p < 0.1.

presents the results of the baseline regression model. In Column (1), the estimated coefficient of new quality productive forces on intelligent technology is 1.804, which is significantly positive at the 5% level, indicating that NQPFs strongly promote the development of intelligent technology in the manufacturing sector. In Column (2), the estimated coefficient of NQPFs on intelligent application is 2.656, which is significant at the 5% level, suggesting that NQPFs exert a robust and positive effect on the diffusion and integration of intelligent applications. In Column (3), the estimated coefficient of NQPFs on intelligent benefits is 3.444, which is significant at the 5% level.

However, in the regression analysis, we found that the significance of some control variables differed. The “degree of government intervention” variable in columns (2) and (3) was not significant in the coefficients of intelligent application and intelligent benefits, despite having a significant negative impact on intelligent technology (−1.171, p < 0.01). This phenomenon may be related to the outbreak of the US–China trade war in 2018. At that time, the United States, in collaboration with its allies, imposed tariffs and trade restrictions on China’s manufacturing sector, resulting in significant short-term pressure on the Chinese economy and thereby affecting the improvement of smart benefits.

5.5. Robustness Test

In this study, to validate the robustness of the NQPF measurement method, principal component analysis (PCA) was employed to recalculate the development level of NQPs, with principal components PC1 and PC2 serving as representative variables for NQPFs. By adopting an alternative measurement approach, we conducted regression tests between PC1 and PC2 and intelligent technology, intelligent applications, and intelligent benefits, respectively. The results indicate that PC1 has a significant positive promotional effect on intelligent technology, intelligent applications, and intelligentization levels at the 1% significance level. Meanwhile, PC2 has a positive promotional effect on intelligent technology and intelligent applications at the 1% significance level, and its impact on intelligent benefits remains positive at the 5% significance level.

Further robustness tests (see

Table 9. Robustness test results.

	(1)	(2)	(3)	(4)	(5)	(6)
	Intelligent Technology	Intelligent Application	Intelligent Benefits	Intelligent Technology	Intelligent Application	Intelligent Benefits
PC1	0.758 **	1.055 ***	1.502 ***
	(0.198)	(0.173)	(0.079)
Degree of Government Intervention	−1.351 ***	−0.498 **	−0.602 ***	−1.351 ***	−0.498 **	−0.602 ***
	(0.218)	(0.156)	(0.083)	(0.218)	(0.156)	(0.083)
Level of Social Consumption	0.323	−0.125	−0.684 **	0.323	−0.125	−0.684 **
	(0.361)	(0.248)	(0.196)	(0.361)	(0.248)	(0.196)
Level of Education expenditure	0.309	−0.372	−0.929 ***	0.309	−0.372	−0.929 ***
	(0.169)	(0.200)	(0.068)	(0.169)	(0.200)	(0.068)
industry Index	−0.135	−0.542 **	−0.473 **	−0.135	−0.542 **	−0.473 **
	(0.344)	(0.211)	(0.162)	(0.344)	(0.211)	(0.162)
labor Index	−0.534	−0.791	−1.037 ***	−0.534	−0.791	−1.037 ***
	(0.655)	(0.478)	(0.238)	(0.655)	(0.478)	(0.238)
PC2				0.758 **	1.055 ***	1.502 ***
				(0.198)	(0.173)	(0.079)
_cons	−0.014	−0.073 ***	−0.082 ***	−0.014	−0.073 ***	−0.082 ***
	(0.018)	(0.016)	(0.007)	(0.018)	(0.016)	(0.007)
N	12	12	12	12	12	12

Note: *** p < 0.01, ** p < 0.05.

) indicate that after replacing the measurement method for new-type productivity, its influence on the dependent variables remains significant and maintains a positive relationship. This result provides strong support for the robustness of the new-type productivity measurement method and validates the validity and rationality of the new-type productivity indicators adopted in this study.

Table 8 shows the regression results of intelligent technology, intelligent application, and intelligent benefits after combining principal component analysis with entropy weighting. Both PC1 and PC2 have a significant impact on the dependent variables, indicating that different measurement methods for new-type productivity can consistently explain the driving forces behind the intelligent transformation of manufacturing.

5.6. Endogeneity Test

In empirical analysis, issues such as reverse causality, omitted variable bias, and measurement error can lead to endogeneity problems. To address this issue, this study systematically tested the model using the control function approach to ensure the robustness of the estimation results. The specific operation consists of two steps:

First, instrumental variable regression was used to regress the new-type productivity level on fiscal expenditure as the instrumental variable (IV). This process not only estimated the relationship between NPQ and the instrumental variable but also extracted the residual term e_NPQ, which captures the potential endogeneity in NPQ.

Next, the control function regression stage was conducted. Regression models were constructed with intelligent technology, intelligent applications, and intelligent benefits as dependent variables, and the residual term e_NPQ extracted in the first step was included as an additional explanatory variable in the models. If the residual term e_NPQ is not significant in the regression results, this indicates that the endogeneity issue in the development level of new-type productivity is not severe, thereby validating the validity of the instrumental variables and the robustness of the model estimation results. Based on the final endogeneity test results, e-NPF is not significant, further confirming that there are no significant endogeneity issues in the models for intelligent technology, intelligent applications, and intelligent benefits. Therefore, we can be confident that the model results of this study are reliable and not affected by endogenous interference (

Table 10. Endogeneity test results.

	(1)	(2)	(3)
	Intelligent Technology	Intelligent Application	Intelligent Benefits
NPQ	1.960 *	2.743 **	3.713 **
	(0.723)	(0.805)	(0.888)
e_NPQ	1.187	0.663	2.043
	(2.263)	(1.570)	(1.024)
Degree of Government Intervention	−1.211 **	−0.306	−0.281
	(0.272)	(0.343)	(0.417)
Level of Social Consumption	0.947 *	0.736	0.622
	(0.400)	(0.388)	(0.593)
Level of Education expenditure	0.349	−0.312	−0.890 **
	(0.274)	(0.295)	(0.254)
labor Index	−0.480	−1.022	−1.136 *
	(0.325)	(0.527)	(0.437)
labor Index	−0.568	−0.846	−1.023
	(0.662)	(0.743)	(0.571)
_cons	0.026	−0.017	−0.003
	(0.016)	(0.010)	(0.012)
N	12	12	12

Note: ** p < 0.05, * p < 0.1.

).

6. Conclusions and Policy Recommendations

This study uses national-level time-series data from 2012 to 2023, then uses principal component analysis and entropy value-TOPSIS methods, evaluation indicator systems for new quality productivity and intelligent transformation of the manufacturing industry were constructed. It then empirically examines the impact of NQPFs on manufacturing intelligentization. The main conclusions are as follows: ① NQPFs drive the intelligent transformation of manufacturing through three core mechanisms: Innovative allocation of production factors, Revolutionary technological breakthroughs, and Deep industrial transformation and upgrading. ② From 2012 to 2023, the development of China’s new quality productive forces showed a slow upward trend. However, this process was not smooth sailing. The complex changes in the domestic and international economic environment had a phased impact on the development of new quality productive forces, resulting in a certain degree of volatility in its development path. The rapid development of new quality productive forces has been driven by strong national policy support, the growth of the digital economy, strategic emerging industry planning, and China’s consistent commitment to high-level opening-up and international cooperation and exchange. At the same time, external factors such as the sudden outbreak of the pandemic and Sino-US trade frictions have also caused significant phased impacts on the development of new quality productive forces, slowing down progress in certain areas and increasing development uncertainties. ③ The intelligent transformation of manufacturing has progressed smoothly, with continuous improvements from 2012 to 2023. This trend has been greatly facilitated by government policy guidance, evolving domestic and global economic environments, and the rapid development of NQPFs. However, from 2020 to 2023, intelligent benefits experienced a slight decline, likely due to the combined effects of the COVID-19 pandemic and U.S.–China trade tensions. ④ NQPFs serve as a powerful driver of intelligent manufacturing transformation. It has a relatively significant positive impact on intelligent technology, intelligent applications, and the benefits of intelligentization benefit. Based on the above findings, the following policy suggestions are proposed:

(1)

Optimizing the internal and external environment to lay a solid foundation for the intelligent transformation of manufacturing

In terms of the domestic economic environment, efforts should focus on systemic reforms and optimizations in both the policy environment and the social and public opinion environment. At the policy level, it is imperative to further refine the policy framework to establish a robust institutional foundation that supports the intelligent transformation of manufacturing. In the social environment, efforts should be made to foster a more inclusive social and public opinion environment, thereby creating favorable external conditions for the intelligent transformation of manufacturing.

In terms of the international economic environment, the intelligent transformation of the manufacturing sector requires a multi-dimensional approach to actively address global challenges and seize international opportunities. Strengthening international cooperation and exchange, monitoring the dynamic changes in international market demand, and enhancing industrial cooperation with countries along the Belt and Road Initiative are all essential. China’s manufacturing sector will be able to steadily advance in the complex and ever-changing international landscape, achieving high-quality development and contributing Chinese wisdom and solutions to the global upgrading of the manufacturing sector.

(2)

Strengthening the three-dimensional mechanism to accelerate the intelligent transformation of the manufacturing industry

Innovative allocation of production factors is the key to promoting high-quality development in the manufacturing sector. We should unlock the potential of traditional factors and data elements, drive the qualitative transformation of traditional factors, tap into the value of data elements, and promote data circulation and cooperation among upstream and downstream enterprises in the industrial chain. We should promote data sharing and openness, advance the marketization of data elements, strengthen data regulation, ensure data security and compliant use, thereby empowering the intelligent transformation of the manufacturing sector and driving it toward higher quality, greater efficiency, and more sustainable development.

Advancing revolutionary technological breakthroughs is the key to building a core competitiveness system. With scientific and technological innovation as the core driving force, we should reshape the manufacturing industry’s innovation system and development landscape, build a core support system for intelligent manufacturing, and reshape the industry’s value creation methods and ecological landscape. We should introduce internationally advanced disruptive technologies to enhance China’s position and competitiveness in the global industrial chain.

Promoting the deep transformation and upgrading of industries is the inevitable path toward a modern manufacturing system. Systematically reconstruct the industrial ecosystem of manufacturing to form new industrial organizational forms and development paradigms. Accelerate the intelligent transformation of industrial clusters, cultivate new business models such as shared manufacturing and personalized customization, and drive the transformation of manufacturing from standardized mass production to flexible and service-oriented production, thereby reshaping the value creation model of the industry. Simultaneously, promote the digital transformation of the entire value chain of the real economy to continuously enhance the international competitiveness and sustainable development capabilities of China’s manufacturing sector.

(3)

Mobilizing collective efforts for transformation and building a collaborative governance ecosystem for transformation.

From a conceptual perspective, the government should take the lead in establishing a multi-party coordination and cooperation mechanism and building a multi-party cooperation platform involving the government, enterprises, universities, and research institutions. From an institutional perspective, the government should improve the legal and regulatory framework for scientific and technological innovation, strengthen intellectual property protection and utilization, and provide a solid institutional guarantee for collaborative innovation among all parties. Through policy measures such as fiscal subsidies and tax incentives, the government should encourage enterprises to increase their R&D investment, reduce innovation costs, and enhance their innovation momentum. At the same time, deepen reforms in the science and technology management system, innovate financial service models, develop new financial products such as intellectual property pledge financing, and increase support for science and technology-specific loans to provide diversified, full-cycle financial support for the transformation and upgrading of manufacturing, fostering a favorable ecosystem driven by policy guidance, market forces, and social participation.

From a technological perspective, a cross-disciplinary training system should be established that integrates basic education, higher education, vocational education, and continuing education to cultivate a high-quality, multidisciplinary talent pool. By establishing a digital information-sharing platform, efficient knowledge resource flow and collaborative innovation can be achieved. Additionally, international talent cooperation should be strengthened through joint research and development, joint training, and other methods to attract global top talent, effectively transforming digital talent reserves into industrial development momentum, and providing robust technological support and talent guarantees for the transformation and upgrading of the manufacturing sector.

7. Discussion

7.1. Research Limitations

The study in this paper has the following limitations: (1) The NQPFs sample time interval was selected as 2011–2023, which is a relatively limited time span and may not fully capture the evolutionary patterns of new quality productivity over a longer cycle. (2) In terms of available data, while national-level data exhibit macro-level consistency, they obscure industry-specific heterogeneity. In the next phase, provincial and municipal-level data will be supplemented to conduct sample-specific tests. (3) Methodologically, while instrumental variables and control functions have been used to mitigate endogeneity, exogenous shocks such as trade wars may still be associated with omitted variables. Additionally, the PCA-linear framework may underestimate the threshold for technological breakthroughs. (4) The entropy-weighted TOPSIS method is highly sensitive to sample fluctuations and outliers, resulting in insufficient stability. Additionally, entropy values themselves are susceptible to the influence of units of measurement and standardization methods, further amplifying the randomness and interpretive biases in evaluation results, potentially leading to errors in measuring the intelligent transformation of manufacturing.

7.2. Future Research Directions

(1) This study primarily relies on time-series data. Future research could expand the scope by constructing multi-level models at the provincial, industry, and enterprise levels, and conducting dynamic tracking surveys of enterprise or industry data to continuously examine their inherent heterogeneity and the impact of new productive forces on the intelligent transformation of manufacturing. (2) Future research will build upon traditional instrumental variable methods and control function methods, further incorporating cutting-edge econometric techniques such as system generalized method of moments (GMM), semi-parametric dual machine learning, and heterogeneous double difference-event study designs to establish a multi-dimensional identification strategy. By treating the “Smart Manufacturing Demonstration Zone” policy as a quasi-natural experiment, the double difference method (DID) will be employed to assess policy effects, ensuring the validity of causal inference. Building on this, the study innovatively combines panel threshold regression models to systematically examine the nonlinear impact of new quality productivity on the intelligent transformation of manufacturing, with a focus on analyzing its threshold effects and the heterogeneity of marginal contributions. (3) Furthermore, a more micro-level-based indicator system for the intelligent transformation of manufacturing is constructed, simultaneously estimating the bidirectional interaction pathways between new quality productivity and intelligent transformation, and examining the impact of intelligent transformation on new quality productivity. Additionally, ABM dynamic simulation is employed to realistically recreate the system evolution and feedback mechanisms under policy shocks, thereby providing more reliable empirical evidence for theoretical expansion and precise policy design.